2009-04-15

ICML 2008的论文集合

转载自 http://www.informatik.uni-trier.de/~ley/db/conf/icml/icml2008.html

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2009-04-09

新买了两本书，回归分析方面的

Classical and Modern Regression with Application(Second Edition)
Nonlinear Regression Analysis and its Application
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2009-04-01

如何将用户信息和电影信息加入到基于CF的推荐系统

在传统的协同过滤中，我们仅仅知道用户对电影的评分。但如果我们还有很多其他信息，比如用户的年龄，国籍，城市，学历，以及电影的导演，演员，简介，标签，等等这些内容信息。那么如果将这些信息加入到CF系统中以获得最好的预测误差？

我目前对这个问题还没有切实的解决办法，能够显著的提高现有的协同过滤的精度。这个其实输入内容过滤和协同过滤的结合。以往基于knn的方法一般用这些信息计算相似度，不过在SVD的方法中如何融入这些信息确实是一个值得研究的问题。
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2009-03-25

宋朝的牛人真多啊

今天下了狠心，买了一套史记，中华书局版的，嘿嘿。

最近看电视，忽然发现北宋的牛人真多啊，而且都集中在一个时间段（100年）。我特地收集了这些牛人的生卒年。列在下面，嘿嘿。

寇准 961-1023
晏殊 991-1055
范仲淹 989-1052
欧阳修 1007-1072
苏洵 1009-1066
司马光 1019-1086
曾巩 1019-1083
王安石 1021-1086
程颢 1032-1085
程颐 1033-1107
苏轼 1037-1101
苏辙 1039-1112

看，牛人们在很短的时候都冒出来了，看来，乱世出英雄啊。和三国差不多。
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2009-03-10

Google Reader分享的订阅



我最近发现google reader的分享提供的rss功能不错，让我们能订阅别人的分享。

这里是我的分享，欢迎大家订阅 http://feeds2.feedburner.com/xlshare

http://www.google.com/reader/shared/05597061783814717014
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2009-03-06

twitter改界面了


看来twitter开始进军搜索了，要做实时网络搜索，很好很强大。可惜现在对中文搜索支持的不好，不知道是搜索技术不行，还是说中文的人少，嘿嘿。
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2009-03-04

最近有关recommender system和collaborative filtering的会议

ACM Recommender Systems 2009
Important Dates:
Long and short papers due: May 8
Author notification: June 19 (6 weeks)
2nd round short papers: June 26
Notification 2nd round: July 17
Camera-ready copy: August 17

Web Intelligence 2009

IMPORTANT DATES
Workshop proposal submission: January 15, 2009
Electronic submission of full papers: April 10, 2009
Tutorial proposal submission: April 10, 2009
Workshop paper submission: April 30, 2009
Notification of paper acceptance: June 3, 2009
Camera-ready copies of accepted papers: June 30, 2009
Workshops: September 15, 2009
Conference: September 15-18, 2009
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2009-02-24

我在NetflixPrize的进展



NetflixPrize是一个collaborative filtering的比赛，目的在于设计出更好的推荐系统。我上周用它的数据集测试了我的算法，因为参数时间还短，目前结果不是很理想。下面将我的方法和一些已知的结果公布一下。

目前我使用的是SVD的方法，用这个方法，是因为这个方法比较快，需要的内存不大(3G)左右。至于kNN的方法，我的计算相似度矩阵的算法还比较耗时（有人说这个步骤可以很快），所以我先尝试了SVD的方法。

我目前的模型用的是最简单的svd模型：
r(u,i) = mean + b(u) + b(i) + <p(u), q(i)>
用梯度下降法优化。
6fa8f2de

在训练时，可以probe数据集里面的数据是包含在train里的，我们计算推广误差的时候，需要在train-probe的数据集上训练。但是在计算quiz的时候，还是要在整个train上做训练，否则精度相差还是很大的。

2009/02/24 d = 250，学习速率0.0055，正则化参数0.002，RMSE = 0.904

2009/02/25
今天用一种新的方法计算item-based算法中的相关系数，只需要3个小时（包含读取文件的时间）。

详细进展和介绍在 http://xlnetflixprize.blogspot.com/
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2009-02-16

推荐系统和协同过滤面临的主要问题









数据稀疏
协同过滤的精度主要取决于用户数据的多少。如果一个系统有很多用户的历史数据，他就能更好的对用户的喜欢做出预测。所以，目前推荐系统做的最好的都是那些有着很大量用户数据的公司，比如Google, Yahoo, Netflix, Amazon等等。但是，即使拥有很多数据，数据还是不够多，因为推荐系统的历史还不够长，还没有积累足够的数据。在目前处理稀疏数据的算法中，软性SVD是一种最好的方法。

新用户问题
这个问题和数据稀疏问题有一些相似性，他是指如何对新用户做出推荐。当一个新用户进入一个网络时，我们对他的兴趣爱好还一无所知，这时如何做出推荐是一个很重要的问题。一般在这个时候，我们只是向用户推荐那写普遍反映比较好的物品，也就是说，推荐完全是基于物品的。

新用户问题还有一个变种就是长尾(long tail)问题，在Amazon中，不是所有的用户都对很多书给出了评分，很多用户只给少数的书给出了评分，这些用户就处在一个长尾中，如何处理那些不太表露自己兴趣的用户，也是推荐系统的一个主要问题。

隐性喜好发现
在现在的推荐系统中，用户的喜欢是通过用户对某些物品进行评分获得的。这种获得用户兴趣的方法是一种很直接的方法。但在实际的互联网中，用户有很多隐性的方法表露他们的喜欢。比如用户的文字评论，我们可以通过自然语言处理从用户的评论中获得用户的兴趣；或者是用户的浏览行为，比如用户长时间的浏览一个物品，或者用户经常浏览一个物品，或者用户
购买了一个物品，这些行为都可以作为模式识别系统中的特征。

所以，发现用户的隐性喜好，相对于模式识别的特征提取，这方面的研究也很热门。

用户兴趣的变化
我们知道，用户的兴趣不是永远不变的，随着年龄和阅历的变化，用户的行为会发生变化。也就是说，协同过滤其实还应该加入一个时间因子。目前对于变化的用户兴趣的研究还处于起步阶段，主要是因为现有的系统历史都不是很久，大多数用户的兴趣还是比较稳定的，但是随着互联网的发展，用户兴趣的变化对推荐系统的影响将会越来越明显，所以这方面的研究也将越来越重要。

偏激的用户和全新的物品
我们知道，这个世界上有一些用户是很偏激的。他们和大多数人的观点是相反的。对于这种用户，现有的推荐系统做出的预测往往是很差的。如何处理偏激的用户，是推荐系统中的一个重要问题。

和偏激用户相对应的，是全新的物品。比如有一部新电影，他是颠覆性的，和以前的电影都不太相似。用户对于这个电影的爱好和用户以前的兴趣是没有太大关系的，因为用户从来没见过这种电影，这个问题也是导致现有的推荐系统精度不高的主要原因。

马太效应以及推荐系统对互联网的影响
我们知道，被推荐系统所推荐的物品将会越来越热门，这就导致了大量很好的物品可能会被推荐系统所淹没。在互联网中，物品实在是太多了，而推荐系统只能推荐有限的物品。解决这个问题的主要方法是增加推荐系统的多样性，比如一个推荐系统发现一个用户非常喜欢吃德芙巧克力，那么他给这个用户推荐10个产品，不需要都是德芙巧克力，也可以推荐别的一些巧克力，或者一些和巧克力相似的甜品。在推荐时，不仅要推荐用户喜欢的东西，而且要通过推荐让用户喜欢一些东西，有的时候，用户自己也不知道他喜欢什么，通过推荐系统，他可能会发现一些新东西他比较喜欢。

推荐系统中的作弊
只要涉及到经济利益，就有人作弊。搜索引擎作弊是一个被研究了很久的问题，因为在搜索引擎中，自己的网站排名越高，就能获得越多的经济利益。在推荐系统中也是如此，比如在淘宝中，如果一个卖家的物品经常被推荐，他就可能获得很多经济利益。这样，很多电子商务的推荐系统都遭受到了作弊的干扰，一些人通过一些技术手段，对自己卖的物品给出非常高的评分，这就是一种作弊行为。

推荐系统中的作弊在电子商务网站中越来越严重，特别是在美国这种互联网比较发达的国家，已经受到一些研究者的重视。作弊行为相当于人为的向系统中注入了噪声。目前解决作弊的算法主要是基于信任度和信用的。现在很多电子商务网站都引入了信用系统，比如淘宝等等。如何设计信用系统和推荐系统更好的融合，是一个重要的研究问题。
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2009-02-09

If You Liked This, You’re Sure to Love That

这是对Netflix的第一名Len Bertoni的采访，其中提到了很多collaborative filtering的方法和主要困难。现在转载如下，并对某些内容给出解释。

THE “NAPOLEON DYNAMITE” problem is driving Len Bertoni crazy. Bertoni is a 51-year-old “semiretired” computer scientist who lives an hour outside Pittsburgh. In the spring of 2007, his sister-in-law e-mailed him an intriguing bit of news: Netflix, the Web-based DVD-rental company, was holding a contest to try to improve Cinematch, its “recommendation engine.” The prize: $1 million.

(Netflix举行了一个推荐系统的比赛，如果谁能将Cinemath的预测结果提高10%，将会获得100万美金，目前最好的提高是9.63%，由Len Bertoni做出)

Cinematch is the bit of software embedded in the Netflix Web site that analyzes each customer’s movie-viewing habits and recommends other movies that the customer might enjoy. (Did you like the legal thriller “The Firm”? Well, maybe you’d like “Michael Clayton.” Or perhaps “A Few Good Men.”) The Netflix Prize goes to anyone who can make Cinematch’s predictions 10 percent more accurate. One million dollars might sound like an awfully big prize for such a small improvement. But in fact, Netflix’s founders tried for years to improve Cinematch, with only incremental results, and they knew that a 10 percent bump would be a challenge for even the most deft programmer. They also knew that, as Reed Hastings, the chief executive of Netflix, told me recently, “getting to 10 percent would certainly be worth well in excess of $1 million” to the company. The competition was announced in October 2006, and no one has won yet, though 30,000 hackers worldwide are hard at work on the problem. Each day, teams submit their updated solutions to the Netflix Prize Web page, and Netflix instantly calculates how much better than Cinematch they are. (There’s even a live “leader board” ranking the top contestants.)

In March 2007, Bertoni decided he wanted to give it a crack. So he downloaded a huge set of data that Netflix put online: an enormous list showing how 480,189 of the company’s customers rated 17,770 Netflix movies. When Netflix customers log into their accounts, they can rate any movie from one to five stars, to help “teach” the Netflix system what their preferences are; the average customer has rated around 200 movies, so Netflix has a lot of information about what its customers like and don’t like. (The data set doesn’t include any personal information — names, ages, location and gender have been stripped out.) So Bertoni began looking for patterns that would predict customer behavior — specifically, an algorithm that would guess correctly the number of stars a given user would apply to a given movie. A year and a half later, Bertoni is still going, often spending 20 hours a week working on it in his home office. His two children — 12 and 13 years old — sometimes sit and brainstorm with him. “They’re very good with mathematics and algebra,” he told me, chuckling. “And they think of interesting questions about your movie-watching behavior.” For example, one day the kids wondered about sequels: would a Netflix user who liked the first two “Matrix” movies be just as likely to enjoy the third one, even though it was widely considered to be pretty dreadful?

Each time he or his kids think of a new approach, Bertoni writes a computer program to test it. Each new algorithm takes on average three or four hours to churn through the data on the family’s “quad core” Gateway computer. Bertoni’s results have gradually improved. When I last spoke to him, he was at No. 8 on the leader board; his program was 8.8 percent better than Cinematch. The top team was at 9.44 percent. Bertoni said he thought he was within striking distance of victory.

But his progress had slowed to a crawl. The more Bertoni improved upon Netflix, the harder it became to move his number forward. This wasn’t just his problem, though; the other competitors say that their progress is stalling, too, as they edge toward 10 percent. Why?

Bertoni says it’s partly because of “Napoleon Dynamite,” an indie comedy from 2004 that achieved cult status and went on to become extremely popular on Netflix. It is, Bertoni and others have discovered, maddeningly hard to determine how much people will like it. When Bertoni runs his algorithms on regular hits like “Lethal Weapon” or “Miss Congeniality” and tries to predict how any given Netflix user will rate them, he’s usually within eight-tenths of a star. But with films like “Napoleon Dynamite,” he’s off by an average of 1.2 stars.

The reason, Bertoni says, is that “Napoleon Dynamite” is very weird and very polarizing. It contains a lot of arch, ironic humor, including a famously kooky dance performed by the titular teenage character to help his hapless friend win a student-council election. It’s the type of quirky entertainment that tends to be either loved or despised. The movie has been rated more than two million times in the Netflix database, and the ratings are disproportionately one or five stars.

(Napoleon Dynamite 是一部电影，人们对这部电影的看法非常两极分化，在Netflix中，这部电影被200万用户打分，但是分数在1-5个等级中杂乱的分布，没有什么规律)

Worse, close friends who normally share similar film aesthetics often heatedly disagree about whether “Napoleon Dynamite” is a masterpiece or an annoying bit of hipster self-indulgence. When Bertoni saw the movie himself with a group of friends, they argued for hours over it. “Half of them loved it, and half of them hated it,” he told me. “And they couldn’t really say why. It’s just a difficult movie.”

Mathematically speaking, “Napoleon Dynamite” is a very significant problem for the Netflix Prize. Amazingly, Bertoni has deduced that this single movie is causing 15 percent of his remaining error rate; or to put it another way, if Bertoni could anticipate whether you’d like “Napoleon Dynamite” as accurately as he can for other movies, this feat alone would bring him 15 percent of the way to winning the $1 million prize. And while “Napoleon Dynamite” is the worst culprit, it isn’t the only troublemaker. A small subset of other titles have caused almost as much bedevilment among the Netflix Prize competitors. When Bertoni showed me a list of his 25 most-difficult-to-predict movies, I noticed they were all similar in some way to “Napoleon Dynamite” — culturally or politically polarizing and hard to classify, including “I Heart Huckabees,” “Lost in Translation,” “Fahrenheit 9/11,” “The Life Aquatic With Steve Zissou,” “Kill Bill: Volume 1” and “Sideways.”

So this is the question that gently haunts the Netflix competition, as well as the recommendation engines used by other online stores like Amazon and iTunes. Just how predictable is human taste, anyway? And if we can’t understand our own preferences, can computers really be any better at it?

IT USED TO BE THAT if you wanted to buy a book, rent a movie or shop for some music, you had to rely on flesh-and-blood judgment — yours, or that of someone you trusted. You’d go to your local store and look for new stuff, or you might just wander the aisles in what librarians call a stack search, to see if anything jumped out at you. You might check out newspaper reviews or consult your friends; if you were lucky, your local video store employed one of those young cinéastes who could size you up in a glance and suggest something suitable.

The advent of online retailing completely upended this cultural and economic ecosystem. First of all, shopping over the Web is not a social experience; there are no clever clerks to ask for advice. What’s more, because they have no real space constraints, online stores like Amazon or iTunes can stock millions of titles, making a stack search essentially impossible. This creates the classic problem of choice: how do you decide among an effectively infinite number of options?

But Web sites have this significant advantage over brick-and-mortar stores: They can track everything their customers do. Every page you visit, every purchase you make, every item you rate — it is all recorded. In the early ’90s, scientists working in the field of “machine learning” realized that this enormous trove of data could be used to analyze patterns in people’s taste. In 1994, Pattie Maes, an M.I.T. professor, created one of the first recommendation engines by setting up a Web site where people listed songs and bands they liked. Her computer algorithm performed what’s known as collaborative filtering. It would take a song you rated highly, find other people who had also rated it highly and then suggest you try a song that those people also said they liked.

“We had this realization that if we gathered together a really large group of people, like thousands or millions, they could help one another find things, because you can find patterns in what they like,” Maes told me recently. “It’s not necessarily the one, single smart critic that is going to find something for you, like, ‘Go see this movie, go listen to this band!’ ”

In one sense, collaborative filtering is less personalized than a store clerk. The clerk, in theory anyway, knows a lot about you, like your age and profession and what sort of things you enjoy; she can even read your current mood. (Are you feeling lousy? Maybe it’s not the day for “Apocalypse Now.”) A collaborative-filtering program, in contrast, knows very little about you — only what you’ve bought at a Web site and whether you rated it highly or not. But the computer has numbers on its side. It may know only a little bit about you, but it also knows a little bit about a huge number of other people. This lets it detect patterns we often cannot see on our own. For example, Maes’s music-recommendation system discovered that people who like classical music also like the Beatles. It is an epiphany that perhaps make sense when you think about it for a second, but it isn’t immediately obvious.

Soon after Maes’s work made its debut, online stores quickly understood the value of having a recommendation system, and today most Web sites selling entertainment products have one. Most of them use some variant of collaborative filtering — like Amazon’s “Customers Who Bought This Item Also Bought” function. Some setups ask you to actively rate products, as Netflix does. But others also rely on passive information. They keep track of your everyday behavior, looking for clues to your preferences. (For example, many music-recommendation engines — like the Genius feature on Apple’s iTunes, Microsoft’s Mixview music recommender or the Audioscrobbler program at Last.fm — can register every time you listen to a song on your computer or MP3 player.) And a few rare services actually pay people to evaluate products; the Pandora music-streaming service has 50 employees who listen to songs and tag them with descriptors — “upbeat,” “minor key,” “prominent vocal harmonies.”

Netflix came late to the party. The company opened for business in 1997, but for the first three years it offered no recommendations. This wasn’t such a big problem when Netflix stocked only 1,000 titles or so, because customers could sift through those pretty quickly. But Netflix grew, and today, it stocks more than 100,000 movies. “I think that once you get beyond 1,000 choices, a recommendation system becomes critical,” Hastings, the Netflix C.E.O., told me. “People have limited cognitive time they want to spend on picking a movie.”

Cinematch was introduced in 2000, but the first version worked poorly — “a mix of insightful and boneheaded recommendations,” according to Hastings. His programmers slowly began improving the algorithms. They could tell how much better they were getting by trying to replicate how a customer rated movies in the past. They took the customer’s ratings from, say, 2001, and used them to predict their ratings for 2002. Because Netflix actually had those later ratings, it could discern what a “perfect” prediction would look like. Soon, Cinematch reached the point where it could tease out some fairly nuanced — and surprising — connections. For example, it found that people who enjoy “The Patriot” also tend to like “Pearl Harbor,” which you’d expect, since they’re both history-war-action movies; but it also discovered that they like the heartstring-tugging drama “Pay It Forward” and the sci-fi movie “I, Robot.”

Cinematch has, in fact, become a video-store roboclerk: its suggestions now drive a surprising 60 percent of Netflix’s rentals. It also often steers a customer’s attention away from big-grossing hits toward smaller, independent movies. Traditional video stores depend on hits; just-out-of-the-theaters blockbusters account for 80 percent of what they rent. At Netflix, by contrast, 70 percent of what it sends out is from the backlist — older movies or small, independent ones. A good recommendation system, in other words, does not merely help people find new stuff. As Netflix has discovered, it also spurs them to consume more stuff.

For Netflix, this is doubly important. Customers pay a flat monthly rate, generally $16.99 (although cheaper plans are available), to check out as many movies as they want. The problem with this business model is that new members often have a couple of dozen movies in mind that they want to see, but after that they’re not sure what to check out next, and their requests slow. And a customer paying $17 a month for only one movie every month or two is at risk of canceling his subscription; the plan makes financial sense, from a user’s point of view, only if you rent a lot of movies. (My wife and I once quit Netflix for precisely this reason.) Every time Hastings increases the quality of Cinematch even slightly, it keeps his customers active.

But by 2006, Cinematch’s improving performance had plateaued. Netflix’s programmers couldn’t go any further on their own. They suspected that there was a big breakthrough out there; the science of recommendation systems was booming, and computer scientists were publishing hundreds of papers each year on the subject. At a staff meeting in the summer of 2006, Hastings suggested a radical idea: Why not have a public contest? Netflix’s recommendation system was powered by the wisdom of crowds; now it would tap the wisdom of crowds to get better too.

AS HASTINGS HOPED, the contest has galvanized nerds around the world. The Top 10 list for the Netflix Prize currently includes a group of programmers in Austria (who are at No. 2), a trained psychologist and Web consultant in Britain who uses his teenage daughter to perform his calculus (No. 9), a lone Ph.D. candidate in Boston who calls himself My Brain and His Chain (a reference to a Ben Folds song; he’s at No. 6) and Pragmatic Theory — two French-Canadian guys in Montreal (No. 3). Nearly every team is working on the prize in its spare time. In October, when I dropped by the house of Martin Chabbert, a 32-year-old member of the Pragmatic Theory duo, it was only 8:30 at night, but we had to whisper: his four children, including a 2-month-old baby, had just gone to bed upstairs. In his small dining room, a laptop sat open next to children’s books like “Les Robots: Au Service de L’homme” and a “Star Wars” picture book in French.

“This is where I do everything,” Chabbert said. “After the kids are asleep and I’ve packed the lunches for school, I come down at 9 in the evening and work until 11 or 12. It was very exciting in the beginning!” He laughed. “It still is, but with the baby now, going to bed at midnight is not a good idea.”

Pragmatic Theory formed last spring, when Chabbert’s longtime friend Martin Piotte — a 43-year-old electrical and computer engineer — heard about the Netflix Prize. Like many of the amateurs trying to win the $1 million, they had no relevant expertise. (“Absolutely no background in statistics that was useful,” Piotte told me ruefully. “Two guys, absolutely no clue.”) But they soon discovered that the Netflix competition is a fairly collegial affair. The company hosts a discussion board devoted to the prize, and competitors frequently help one another out — discussing algorithms they’ve tried and publicly brainstorming new ways to improve their work, sometimes even posting reams of computer code for anyone to use. When someone makes a breakthrough, pretty soon every other team is aware of it and starts using it, too. Piotte and Chabbert soon learned the major mathematical tricks that had propelled the leading teams into the Top 10.

The first major breakthrough came less than a month into the competition. A team named Simon Funk vaulted from nowhere into the No. 4 position, improving upon Cinematch by 3.88 percent in one fell swoop. Its secret was a mathematical technique called singular value decomposition. It isn’t new; mathematicians have used it for years to make sense of prodigious chunks of information. But Netflix never thought to try it on movies.

Singular value decomposition works by uncovering “factors” that Netflix customers like or don’t like. Say, for example, that “Sleepless in Seattle” has been rated by 200,000 Netflix users. In one sense, this is just a huge list of numbers — user No. 452 gave it two stars; No. 985 gave it five stars; and so on. But you could also think of those ratings as individual reactions to various aspects of the movie. “Sleepless in Seattle” is a “chick flick,” a comedy, a star vehicle for Tom Hanks; each customer is reacting to how much — or how little — he or she likes “chick flicks,” comedies and Tom Hanks. Singular value decomposition takes the mass of Netflix data — 17,770 movies, ratings by 480,189 users — and automatically sorts the films. The programmers do not actively tell the computer what to look for; they just run the algorithm until it groups together movies that share qualities with predictive value.

Sometimes when you look at the clusters of movies, you can deduce the connections. Chabbert showed me one list: at the top were “Sleepless in Seattle,” “Steel Magnolias” and “Pretty Woman,” while at the bottom were “Star Trek” movies. Clearly, the computer recognized some factor that suggests that someone who likes the romantic aspect of “Pretty Woman” will probably like “Sleepless in Seattle” and dislike “Star Trek.” Chabbert showed me another cluster: this time DVD collections of the TV show “Friends” all clustered at the top of the list, while action movies like “Reindeer Games” and thrillers like “Hannibal” clustered at the bottom. Most likely, the computer had selected for “comic” content here. Other lists appear to group movies based on whether they lean strongly to the ideological right or left.

As programmers extract more and more values, it becomes possible to draw exceedingly sophisticated correlations among movies and hence to offer incredibly nuanced recommendations. “We’re teasing out very subtle human behaviors,” said Chris Volinsky, a scientist with AT&T in New Jersey who is one of the most successful Netflix contestants; his three-person team held the No. 1 position for more than a year. His team relies, in part, on singular value decomposition. “You can find things like ‘People who like action movies, but only if there’s a lot of explosions, and not if there’s a lot of blood. And maybe they don’t like profanity,’ ” Volinsky told me when we spoke recently. “Or it’s like ‘I like action movies, but not if they have Keanu Reeves and not if there’s a bus involved.’ ”

MOST OF THE LEADING TEAMS competing for the Netflix Prize now use singular value decomposition. Indeed, given how quickly word of new breakthroughs spreads among the competitors, virtually every team in the Top 10 makes use of similar mathematical ploys. The only thing that separates their scores is how skillfully they tweak their algorithms. The Netflix Prize has come to resemble a drag race in which everyone drives the same car, with only tiny modifications to the fuel injection. Yet those tweaks are crucial. Since the top teams are so close — there is less than a tenth of a percent between each contender — even tiny improvements can boost a team to the top of the charts.

These days, the competitors spend much of their time thinking deeply about the math and psychology behind recommendations. For example, the teams are grappling with the problem that over time, people can change how sternly or leniently they rate movies. Psychological studies show that if you ask someone to rate a movie and then, a month later, ask him to do so again, the rating varies by an average of 0.4 stars. “The question is why,” Len Bertoni said to me. “Did you just remember it differently? Did you see something in between? Did something change in your life that made you rethink it?” Some teams deal with this by programming their computers to gradually discount older ratings.

Another common problem is identifying overly punitive raters. If you’re a really harsh critic and I’m a much more easygoing one, your two-star rating may be equal to my four-star rating. To compensate, an algorithm might try to detect when a Netflix customer tends to hand out only one- or two-star ratings — a sign of a strict, pursed-lip customer — and artificially boost his or her ratings by a half-star or so. Then there’s the problem of movie raters who simply aren’t consistent. They might be evenhanded most of the time, but if they log into Netflix when they’re in a particularly bad mood, they might impulsively decide to rate a couple of dozen movies harshly.

TV shows, which are hot commodities on Netflix, present yet another perplexing issue. Customers respond to TV series much differently than they do to movies. People who loved the first two seasons of “The Wire” might start getting bored during the third but keep on watching for a while, then stop abruptly. So when should Cinematch stop recommending “The Wire”? When do you tell someone to give up on a TV show?

Interestingly, the Netflix Prize competitors do not know anything about the demographics of the customers whose taste they’re trying to predict. The teams sometimes argue on the discussion board about whether their predictions would be better if they knew that customer No. 465 is, for example, a 23-year-old woman in Arizona. Yet most of the leading teams say that personal information is not very useful, because it’s too crude. As one team pointed out to me, the fact that I’m a 40-year-old West Village resident is not very predictive. There’s little reason to think the other 40-year-old men on my block enjoy the same movies as I do. In contrast, the Netflix data are much more rich in meaning. When I tell Netflix that I think Woody Allen’s black comedy “Match Point” deserves three stars but the Joss Whedon sci-fi film “Serenity” is a five-star masterpiece, this reveals quite a lot about my taste. Indeed, Reed Hastings told me that even though Net­flix has a good deal of demographic information about its users, the company does not currently use it much to generate movie recommendations; merely knowing who people are, paradoxically, isn’t very predictive of their movie tastes.

As the teams have grown better at predicting human preferences, the more incomprehensible their computer programs have become, even to their creators. Each team has lined up a gantlet of scores of algorithms, each one analyzing a slightly different correlation between movies and users. The upshot is that while the teams are producing ever-more-accurate recommendations, they cannot precisely explain how they’re doing this. Chris Volinsky admits that his team’s program has become a black box, its internal logic unknowable.

There’s a sort of unsettling, alien quality to their computers’ results. When the teams examine the ways that singular value decomposition is slotting movies into categories, sometimes it makes sense to them — as when the computer highlights what appears to be some essence of nerdiness in a bunch of sci-fi movies. But many categorizations are now so obscure that they cannot see the reasoning behind them. Possibly the algorithms are finding connections so deep and subconscious that customers themselves wouldn’t even recognize them. At one point, Chabbert showed me a list of movies that his algorithm had discovered share some ineffable similarity; it includes a historical movie, “Joan of Arc,” a wrestling video, “W.W.E.: SummerSlam 2004,” the comedy “It Had to Be You” and a version of Charles Dickens’s “Bleak House.” For the life of me, I can’t figure out what possible connection they have, but Chabbert assures me that this singular value decomposition scored 4 percent higher than Cinematch — so it must be doing something right. As Volinsky surmised, “They’re able to tease out all of these things that we would never, ever think of ourselves.” The machine may be understanding something about us that we do not understand ourselves.

Yet it’s clear that something is still missing. Volinsky’s momentum has slowed down significantly, as everyone else’s has. There’s some X factor in human judgment that the current bunch of algorithms isn’t capturing when it comes to movies like “Napoleon Dynamite.” And the problem looms large. Bertoni is currently at 8.8 percent; he says that a small group of mainly independent movies represents more than half of the remaining errors in the way of winning the prize. Most teams suspect that continuing to tweak existing algorithms won’t be enough to get to 10 percent. They need another breakthrough — some way to digitally replicate the love/hate dynamic that governs hard-to-pigeonhole indie films.

“This last half-percent really is the Mount Everest,” Volinsky said. “It’s going to take one of these ‘aha’ moments.”

SOME COMPUTER SCIENTISTS think the “Napoleon Dynamite” problem exposes a serious weakness of computers. They cannot anticipate the eccentric ways that real people actually decide to take a chance on a movie.

The Cinematch system, like any recommendation engine, assumes that your taste is static and unchanging. The computer looks at all the movies you’ve rated in the past, finds the trend and uses that to guide you. But the reality is that our cultural tastes evolve, and they change in part because we interact with others. You hear your friends gushing about “Mad Men,” so eventually — even though you have never had any particular interest in early-’60s America — you give it a try. Or you go into the video store and run into a particularly charismatic clerk who persuades you that you really, really have to give “The Life Aquatic With Steve Zissou” a chance.

As Gavin Potter, a Netflix Prize competitor who lives in Britain and is currently in ninth place, pointed out to me, a computerized recommendation system seeks to find the common threads in millions of people’s recommendations, so it inherently avoids extremes. Video-store clerks, on the other hand, are influenced by their own idiosyncrasies. Even if they’re considering your taste to make a suitable recommendation, they can’t help relying on their own sense of what’s good and bad. They’ll make more mistakes than the Netflix computers — but they’re also more likely to have flashes of inspiration, like pointing you to “Napoleon Dynamite” at just the right moment.

“If you use a computerized system based on ratings, you will tend to get very relevant but safe answers,” Potter says. “If you go with the movie-store clerk, you will get more unpredictable but potentially more exciting recommendations.”

Another critic of computer recommendations is, oddly enough, Pattie Maes, the M.I.T. professor. She notes that there’s something slightly antisocial — “narrow-minded” — about hyperpersonalized recommendation systems. Sure, it’s good to have a computer find more of what you already like. But culture isn’t experienced in solitude. We also consume shows and movies and music as a way of participating in society. That social need can override the question of whether or not we’ll like the movie.

“You don’t want to see a movie just because you think it’s going to be good,” Maes says. “It’s also because everyone at school or work is going to be talking about it, and you want to be able to talk about it, too.” Maes told me that a while ago she rented a “Sex and the City” DVD from Netflix. She suspected she probably wouldn’t really like the show. “But everybody else was constantly talking about it, and I had to know what they were talking about,” she says. “So even though I would have been embarrassed if Netflix suggested ‘Sex and the City’ to me, I’m glad I saw it, because now I get it. I know all the in-jokes.”

Maes suspects that in the future, computer-based reasoning will become less important for online retailers than social-networking tools that tap into the social zeitgeist, that let customers see, in Facebook fashion, for example, what their close friends are watching and buying. (Potter has an even more intriguing idea. He says he thinks that a recommendation system could predict cultural microtrends by monitoring news events. His research has found, for example, that people rent more movies about Wall Street when the stock market drops.) In the world of music, there are already several innovative recommendation services that try to analyze buzz — by monitoring blogs for repeated mentions of up-and-coming bands, or by sifting through millions of people’s playlists to see if a new band is suddenly getting a lot of attention.

Of course, for a company like Netflix, there’s a downside to pushing exciting-but-risky movie recommendations on viewers. If Netflix tries to stretch your taste by recommending more daring movies, it also risks annoying customers. A bad movie recommendation can waste an evening.

Is there any way to find a golden mean? When I put the question to Reed Hastings, the Netflix C.E.O., he told me he suspects that there won’t be any simple answer. The company needs better algorithms; it needs breakthrough techniques like singular value decomposition, with the brilliant but inscrutable insights it enables. But Hastings also says he thinks Maes is right, too, and that social-networking tools will become more useful. (Netflix already has one, in fact — an application that lets users see what their family and peers are renting. But Hastings admits it hasn’t been as valuable as computerized intelligence; only a very small percentage of rentals are driven by what friends have chosen.) Hastings is even considering hiring cinephiles to watch all 100,000 movies in the Netflix library and write up, by hand, pages of adjectives describing each movie, a cloud of tags that would offer a subjective view of what makes films similar or dissimilar. It might imbue Cinematch with more unpredictable, humanlike intelligence.

“Human beings are very quirky and individualistic, and wonderfully idiosyncratic,” Hastings says. “And while I love that about human beings, it makes it hard to figure out what they like.”

Clive Thompson, a contributing writer for the magazine, writes frequently about technology.
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Recommendation Systems: An Interview with Satnam Alag

In a recent post, we looked at recommendation systems, briefly reviewing how Amazon and Google have implemented their own systems for recommending products and content to their users.

We had the opportunity to speak with Satnam Alag, author of the recently published Collective Intelligence in Action, about what makes for a good recommendation system, where the technology is heading, and why Netflix is finding it so hard to improve its own system.

Disclosure: I wrote the forward to 'Collective Intelligence in Action', however I have absolutely no financial interest in the book.

ReadWriteWeb: In our recent post about Netflix, we identified four main approaches to recommendations: Personalized recommendation: based on prior behavior of the user; Social recommendation: based on prior behavior of similar users; Item recommendation: based on the item itself; And a combination of all three. Do you agree with the four approaches we laid out in our article?

Satnam: Those four categories are pretty comprehensive. I present an alternate classification of recommendation systems in my book. I lay out two fundamental approaches. The first approach, item-based analysis, determines items that are related to a particular item. When a user likes a particular item, related ones are recommended. The second approach, user-based analysis, first determines users who are similar to that user.

Further, there are two main approaches to finding similar items and similar users. For the first, content-based analysis, content associated with the item, especially text, is used to compute similarity. In the second, the collaborative approach, actions such as ratings, bookmarking, and so forth are used to find similar items. For the second, user-based analysis, a number of approaches have been taken, including ones based on profile information, user actions, and lists of the user's friends or contacts. Of course, you can combine any these item/user and content/collaborative approaches to build a recommendation system.

The dimensions of the particular item and user space are helpful in deciding whether to use an item-based or user-based approach. Typically, an item-based approach is used to bootstrap one's application when the number of users is small. As the user base grows, the item-based approach is augmented by a user-based approach.

ReadWriteWeb: Other than Amazon and Netflix, which Internet companies have most impressed you in their implementation of recommendation systems?

Satnam: Other than Amazon and Nextflix, Google News' personalization is my personal favorite. Google News is a good example of building a scalable recommendation system for a large number of users (several million unique visitors per month) and a large number of items (several million new stories every two months), with constant item churn. This is different from Amazon's, whose rate of item churn is much lower. Google decided to use collaborative filtering for its recommendation system mainly because of its access to the data of its large user base and because this same approach could be applied to other applications, countries, and languages. A content-based recommendation system perhaps could have worked just as well, but may have required language- or location-specific tweaking. Google also wanted to leverage the same collaborative filtering technology to be able to recommend images, videos, and music, for which it's more difficult to analyze the underlying content.

Among start-ups, my personal favorite is the one we are developing at my current company, NextBio. It's not available yet but should be next month. The key point about this particular recommendation engine is its strong use of an ontology, similar in concept to tags, to develop a common vocabulary for items and users. The system then makes use of profile information and user interactions, both short- and long-term, to provide recommendations. The system leverages both item- and user-based approaches.

ReadWriteWeb: What commercial opportunities do you forsee with recommendation systems over the next few years?

Satnam: A good personalized recommendation system can mean the difference between a successful and a failed website. Given that most applications now invite users to interact and to leverage user-generated content, new content is being generated at a phenomenal rate. Showing the right content to the right user at the right time is key to creating a sticky application. I would be surprised if most successful websites did not leverage recommendation systems to provide personalized experiences to their users.

ReadWriteWeb: Your book includes a discussion of collaborative filtering. Can you tell us a bit about how this fits into the overall picture of recommendation systems?

Satnam: In recent years, an increasing amount of user interaction has provided applications with a large amount of information that can be converted into intelligence. This interaction may be in the form of ratings, blog entries, item tagging, user connections, or shared items of interest. This has led to the problem of information overload. What we need is a system that can recommend items based on the user's interests and interactions. This is where personalization and recommendation engines come in.

In my book, I take a holistic view of adding intelligence to one's application, a recommendation engine being one way to do it. The book focuses on both content-based and collaborative approaches to building recommendation systems. It focuses on capturing relevant information about the user, information from both within and outside one's application, and converting it into recommendations. One of the things you mentioned in your write-up on recommendation systems is that you would like to apply such a system to your website to recommend things to users. Someone reading my book should be able to create such a system using the techniques I demonstrate.

Next Page: Satnam's thoughts on the Netflix Prize and whether the 10% mark will ever be reached.

ReadWriteWeb: Netflix is offering $1 million to the team that can improve its recommendation algorithm by 10%. It's been over 2 years now, with the leading company at 9.63%. There is some skepticism, though, that 10% will be reached anytime soon, because now the contestants are making only incremental progress. Do you expect the 10% mark to be reached soon?

Satnam: Netflix's recommendation engine, Cinematch, uses an item-to-item algorithm (similar to Amazon's) with a number of heuristics. Given that Netflix' recommendation system has been very successful in the real world, it is pretty impressive that teams have been able to improve on it by as much as 9.63%. Of course, the Netflix competition doesn't take into account speed of implementation or the scalability of the approach. It simply focuses on the quality of recommendations in terms of closing the gap between user rating and predicted rating. So, it isn't clear whether Netflix will be able to leverage all of the innovation coming out of this competition. Also, the Netflix data doesn't contain much information to allow for a content-based approach; it's for this reason that teams are focusing on collaborative-based techniques.

The challenges to reaching the 10% mark are:

Skewed data: The data set for the competition consists of more than 100 million anonymous movie ratings, using a scale of one to five stars, made by 480,000 users for 17,770 movies. Note that the user-item data set for this problem is sparsely populated, with nearly 99% of user-item entries being zero. The distribution of movies per user is skewed. The median number of ratings per user is 93. About 10% of users rated 16 or fewer movies, while 25% of users rated 36 or fewer. Two users rated as many as 17,000 movies. Similarly, the ratings per movie are also skewed: almost half the user base rated one popular movie (Miss Congeniality); about 25% of movies had 190 or fewer ratings; and a handful of movies were rated fewer than 10 times.

The approach: The winning team, BellKor, spent more than 2,000 combined hours poring over data to find the winning solution. The winning solution was a linear combination of 107 sets of predictions. Many of the algorithms involved either the nearest-neighbor method (k-NN) or latent factor models, such as SVD/factorization and Restricted Boltzmann Machines (RBMs).

The winning solution uses k-NN to predict the rating for a user, using both the Pearson-r correlation and cosine methods to compute the similarities, with corrections to remove item-specific and user-specific biases. Latent semantic models are also widely used in the winning solution.

The BellKor team found it important to use a variety of models that compensated for each other's shortcomings. No one model alone could have gotten the BellKor team to the top of the competition. The combined set of models achieved an improvement of 8.43% over Cinematch, while the best model -- a hybrid of k-NN applied to output from RBMs -- improved the result by 6.43%. The biggest improvement by LSI methods was 5.1%, with the best pure k-NN model scoring below that. (K for the k-NN methods was in the range of 20 to 50.) The BellKor team also applied a number of heuristics to further improve the results.

The BellKor team demonstrates a number of guidelines for building a winning solution to this kind of competition:

  • Combining complementary models helps improve the overall solution. Note that a linear combination of three models, one each for k-NN, LSI, and RBM, would have yielded fairly good results, an improvement of 7.58%.
  • A principled approach is needed to optimize the solution.
  • The key to winning is building models that can accurately predict when there is sufficient data, without over-applying in the absence of adequate data.

The final solution will be along the same lines, combining multiple models with heuristics. Contestants will probably reach the magic 10% mark in the next year or two.

ReadWriteWeb: Some people think the 10% mark can't be reached with algorithms alone, but that the "human" element is required. For example, ClerkDogs is a service that hires actual former video-store clerks to "create a database that is much richer and deeper than the collaborative filtering engines." It's a similar approach to that of Pandora, which has 50 employees who listen to and tag songs. How far do you think algorithms can go in making recommendations?

Satnam: Recommendation systems are not perfect. A number of elements go into making successful ones, including approach, the speed of computing results, heuristics, the exploration and exploitation of coefficients, and so on. But it has been shown in the real world that the more personalized you can make recommendations, the higher the click-through rate, the stickier the application, and the lower the bounce rate.

Using humans to form a rich database for recommendations may work for small applications, but it would probably be too expensive to scale. I don't see them competing against each other, human versus machine. Even with human/expert recommendations, one first needs to find a human/expert with tastes similar to those of the user, especially if you want to go after the long tail.

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2009-02-07

推荐系统中的两种不同类型的用户

在设计推荐系统时，我们发现有两种用户，一种用户可以称为大众化用户，这种用户喜欢的物品是大家都喜欢的物品。 还有一种用户是偏激的用户，他们喜欢的是大家不怎么喜欢的东西，他们的看法总是和大众化看法相反。

在设计推荐系统时，要充分考虑这两种用户的特点
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