Netflix推荐系统(第二部分)—— 排序

标签: 未分类 | 发表时间:2012-06-23 13:51 | 作者:xlvector


In part one of this blog post, we detailed the different components of Netflix personalization. We also explained how Netflix personalization, and the service as a whole, have changed from the time we announced the Netflix Prize.The $1M Prize delivered a great return on investment for us, not only in algorithmic innovation, but also in brand awareness and attracting stars (no pun intended) to join our team. Predicting movie ratings accurately is just one aspect of our world-class recommender system. In this second part of the blog post, we will give more insight into our broader personalization technology. We will discuss some of our current models, data, and the approaches we follow to lead innovation and research in this space.

在第一部分,我们讨论了Netflix个性化推荐系统的不同应用。我们也解释了Netflix的推荐系统自从我们宣布Netflix Prize后是如何变化的。100万美元的比赛对Netflix无论是在算法的创新还是在宣传Netflix,帮助招聘上都带来了很大的帮助。不过,准确的预测电影的评分只是我们系统的一方面,在第二部分,我们将会更加深入的讨论个性化推荐技术,我们会讨论一下我们用到的模型,数据和方法。

Ranking 排序

The goal of recommender systems is to present a number of attractive items for a person to choose from. This is usually accomplished by selecting some items and sorting them in the order of expected enjoyment (or utility). Since the most common way of presenting recommended items is in some form of list, such as the various rows on Netflix, we need an appropriate ranking model that can use a wide variety of information to come up with an optimal ranking of the items for each of our members.


If you are looking for a ranking function that optimizes consumption, an obvious baseline is item popularity. The reason is clear: on average, a member is most likely to watch what most others are watching. However, popularity is the opposite of personalization: it will produce the same ordering of items for every member. Thus, the goal becomes to find a personalized ranking function that is better than item popularity, so we can better satisfy members with varying tastes.


Recall that our goal is to recommend the titles that each member is most likely to play and enjoy. One obvious way to approach this is to use the member’s predicted rating of each item as an adjunct to item popularity. Using predicted ratings on their own as a ranking function can lead to items that are too niche or unfamiliar being recommended, and can exclude items that the member would want to watch even though they may not rate them highly. To compensate for this, rather than using either popularity or predicted rating on their own, we would like to produce rankings that balance both of these aspects. At this point, we are ready to build a ranking prediction model using these two features.



There are many ways one could construct a ranking function ranging from simple scoring methods, to pairwise preferences, to optimization over the entire ranking. For the purposes of illustration, let us start with a very simple scoring approach by choosing our ranking function to be a linear combination of popularity and predicted rating. This gives an equation of the form frank(u,v) = w1 p(v) + w2 r(u,v) + b, where u=user, v=video item, p=popularity and r=predicted rating. This equation defines a two-dimensional space like the one depicted below.


Once we have such a function, we can pass a set of videos through our function and sort them in descending order according to the score. You might be wondering how we can set the weights w1 and w2 in our model (the bias b is constant and thus ends up not affecting the final ordering). In other words, in our simple two-dimensional model, how do we determine whether popularity is more or less important than predicted rating? There are at least two possible approaches to this. You could sample the space of possible weights and let the members decide what makes sense after many A/B tests. This procedure might be time consuming and not very cost effective. Another possible answer involves formulating this as a machine learning problem: select positive and negative examples from your historical data and let a machine learning algorithm learn the weights that optimize your goal. This family of machine learning problems is known as “Learning to rank” and is central to application scenarios such as search engines or ad targeting. Note though that a crucial difference in the case of ranked recommendations is the importance of personalization: we do not expect a global notion of relevance, but rather look for ways of optimizing a personalized model.
当我们有了这个排序函数后,我们可以将视频按照这个函数进行排序。不过很多人可能会好奇,我们是如何知道这两个因素的线性融合系数的。有两种方法可以找到这个用户系数,第一种是遍历所有的权重可能,并通过AB测试找到最好的权重。这种方法优化速度很慢,效率很低。第二种方式将这个问题转化为一个极其学习的问题,通过收集日志找到正样本和负样本,然后通过一种叫做Learning to rank (LTR)的方法找到这个系数。LTR被广泛的用于搜索引擎和精准广告投放中。

As you might guess, apart from popularity and rating prediction, we have tried many other features at Netflix. Some have shown no positive effect while others have improved our ranking accuracy tremendously. The graph below shows the ranking improvement we have obtained by adding different features and optimizing the machine learning algorithm.


Many supervised classification methods can be used for ranking. Typical choices include Logistic Regression, Support Vector Machines, Neural Networks, or Decision Tree-based methods such as Gradient Boosted Decision Trees (GBDT). On the other hand, a great number of algorithms specifically designed for learning to rank have appeared in recent years such as RankSVM or RankBoost. There is no easy answer to choose which model will perform best in a given ranking problem. The simpler your feature space is, the simpler your model can be. But it is easy to get trapped in a situation where a new feature does not show value because the model cannot learn it. Or, the other way around, to conclude that a more powerful model is not useful simply because you don’t have the feature space that exploits its benefits.







Movie/Video Recommendation : Jinni, GetGlue, Netflix …
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Photos of The Ensemble from Netflix Prize

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