Burst the filter bubble: Bayesian nonparametrics for recommender systems

推荐系统的目标是根据个人喜欢的物品/人向他们提供自动的有针对性的推荐。在过去的几年里，由于著名的Netflix奖，它们获得了很多关注，现在无处不在，被亚马逊、苹果或Youtube等公司使用。推荐系统在商业上具有特殊的经济利益。在这种情况下，给定客户购买的一组观察到的商品，我们的目标是向给定产品的潜在买家提供相关的推荐。人们也可能对获得客户和/或产品的市场细分，以及确定产品流行程度演变的趋势感兴趣。推荐系统出现在新闻、音乐、书籍、网络搜索或餐馆等多个应用领域。当观察商品的受欢迎程度时，数据集通常会表现出严重的尾部行为：大多数购买只涉及少数非常受欢迎的商品，而大多数商品很少被购买。推荐系统根据用户口味提供个性化推荐的能力尤其具有吸引力；但这种个性化也引发了一些关于推荐系统的担忧。其中之一是由Eli Pariser在最近的一本畅销书中创造的“过滤泡沫”一词：担心个性化推荐就像一个回音室，只推荐最受欢迎和最接近用户口味的产品，而不让他接触到矛盾/打破传统的观点或异国/不寻常的产品。对于产品的推荐，这可能会产生负面影响，只推荐用户已经知道的热门产品。最近，计算机科学文献中有大量的研究实际上是致力于开发支持多样性和偶然性的推荐系统。关于新闻和网络搜索，这种效应有时被称为“信息茧”，一些人认为这种效应是对民主的威胁。英国脱欧公投几天后，《卫报》新闻与媒体总编辑卡塔琳娜·维纳（Katharina Viner）就这个问题写了一篇长文。她说明她的观点的博客汤姆·斯坦伯格英国互联网活动家和不是创始人:“我积极通过Facebook寻找人们庆祝Brexit离开胜利,但过滤泡沫是如此强大,和到目前为止延伸至诸如Facebook的自定义搜索,我找不到那些快乐*尽管超过一半的国家显然是欢欣鼓舞的今天*,尽管我*积极*希望听到他们在说什么。”目前，关于这种算法过滤泡沫是否真的比典型的“现实生活”泡沫更强，存在一场争论。无论这在目前是否正确，获得能够公平地表示个人可能接触到的各种项目/意见的推荐算法，或者能够选择支持多样性或偶然性而不是准确性的指标，都是最基本的。任何具有此类目标的算法都必须充分处理数据集的稀有产品和重尾属性。该项目的目标是在一个理论基础和可解释的统计框架中提供这样一种方法。

Recommender systems aim at providing automated targeted recommendations to individuals based on items/people they like. They have gained a lot of attention over the past few years thanks to the famous Netflix prize, and are now ubiquitous and used by companies like Amazon, Apple or Youtube. Recommender systems are of particular economic interest in business. In this case, given an observed set of purchased items by customers, we aim at providing relevant recommendations to potential buyers of a given product. One may also be interested in obtaining a market segmentation of the customers and/or products, and in identifying trends in the evolution of the popularity of products. Recommender systems arise in several application domains for news, music, books, web searches or restaurants. When looking at the popularity of the items, the datasets often exhibit a heavy tail behavior: most purchases concern only a small number of very popular items, the majority of the items being bought very rarely. The ability of recommender systems to provide personalized recommendations tailored to the user tastes are particularly attractive; but this personalization has raised a number of concerns regarding recommender systems. One of them has been popularized under the term "Filter bubble", coined by Eli Pariser in a recent popular book: the fear that personalized recommendations are acting as an echo chamber, only suggesting items which are the most popular and the closest to the user's tastes and not exposing him to contradictory/iconoclastic opinions or exotic/unusual products. Regarding the recommendation of products, this may have the negative effect to only recommend popular items users already know about. A significant amount of research in the computer science literature has actually recently be devoted to deriving recommender systems favoring diversity and serendipity. Regarding news and web searches, this effect is sometimes called ``Information cocoon", and some see this effect as a threat for democracy. A few days after the Brexit vote, Katharina Viner, the Editor-in-Chief of Guardian News \& Media, wrote a long article on this issue. She illustrated her point with the blog post of Tom Steinberg, a British internet activist and mySociety founder:"I am actively searching through Facebook for people celebrating the Brexit leave victory, but the filter bubble is SO strong, and extends SO far into things like Facebook's custom search that I can't find anyone who is happy *despite the fact that over half the country is clearly jubilant today* and despite the fact that I'm *actively* looking to hear what they are saying."There is currently a debate on whether or not this algorithmic filter bubble is actually stronger or not than the typical "real-life" bubble. Whether or not this is currently true, it is primordial to derive recommendation algorithms that are able to provide a fair representation of the diverse set of items/opinions an individual may be exposed to, or to potentially be able to choose metrics that favor diversity or serendipity instead of accuracy. Any algorithm with such objectives has to adequately handle the rare products and the heavy tail properties of the datasets. The objective of this project is to provide such a method, in a theoretically grounded and interpretable statistical framework.