推荐系统的5个问题

by Richard MacManus

http://www.readwriteweb.com/archives/5_problems_of_recommender_systems.php

1. Lack of Data 数据缺失

Perhaps the biggest issue facing recommender systems is that they need a lot of data to effectively make recommendations. It's no coincidence that the companies most identified with having excellent recommendations are those with a lot of consumer user data: Google, Amazon, Netflix, Last.fm.

推荐系统的最大问题是它们需要很大的数据量来做出好的推荐。毫无疑问，那些在推荐系统上做出很好结果的公司都是拥有大量用户数据的公司:Google, Amazon, Netflix, Last.fm.

2. Changing Data 数据变化

This issue was pointed out in ReadWriteWeb's comments by Paul Edmunds, CEO of 'intelligent recommendations' company Clicktorch. Paul commented that systems are usually "biased towards the old and have difficulty showing new".

An example of this was blogged by David Reinke of StyleHop, a resource and community for fashion enthusiasts. David noted that "past behavior [of users] is not a good tool because the trends are always changing" (emphasis ours). Clearly an algorithmic approach will find it difficult if not impossible to keep up with fashion trends. Most fashion-challenged people - I fall into that category - rely on trusted fashion-conscious friends and family to recommend new clothes to them.

David Reinke went on to say that "item recommendations don't work because there are simply too many product attributes in fashion and each attribute (think fit, price, color, style, fabric, brand, etc) has a different level of importance at different times for the same consumer." He did point out though that social recommenders may be able to 'solve' this problem.

3. Changing User Preferences 用户兴趣的变化

Again suggested by Paul Edmunds, the issue here is that while today I have a particular intention when browsing e.g. Amazon - tomorrow I might have a different intention. A classic example is that one day I will be browsing Amazon for new books for myself, but the next day I'll be on Amazon searching for a birthday present for my sister (actually I got her a gift card, but that's beside the point).

On the topic of user preferences, recommender systems may also incorrectly label users - a la this classic Wall St Journal story from 2002, If TiVo Thinks You Are Gay, Here's How to Set It Straight.

4. Unpredictable Items 不可预测的项目

In our post on the Netflix Prize, about the $1 Million prize offered by Netflix for a third party to deliver a collaborative filtering algorithm that will improve Netflix's own recommendations algorithm by 10%, we noted that there was an issue with eccentric movies. The type of movie that people either love or hate, such as Napoleon Dynamite. These type of items are difficult to make recommendations on, because the user reaction to them tends to be diverse and unpredictable.

Music is full of these items. Would you have guessed that this author is a fan of both Metallica and The Carpenters? I doubt Last.fm would make that recommendation.

5. This Stuff is Complex! 系统很复杂

We're stating the obvious here, but the below slide from Strands' presentation at Recked illustrates that it takes a lot of variables to do even the simplest recommendations (and we imagine the below variables only scratch the surface):