Recommender Systems: An Overview

regularly in e-commerce settings. A user, Jane, visits her favorite online bookstore. The homepage lists current best-sellers and also a list containing recommended items. This list might include, for example, a new book published by one of Jane’s favorite authors, a cookbook by a new author, and a supernatural thriller. Whether Jane will find these suggestions useful or distracting is a function of how well they match her tastes. Is the cookbook for a style of cuisine that she likes (and is it different enough from ones she already owns)? Is the thriller too violent? A key feature of a recommender system therefore is that it provides a personalized view of the data, in this case, the bookstore’s inventory. If we take away the personalization, we are left with the list of best-sellers — a list that is independent of the user. The aim of the recommender system is to lower the user’s search effort by listing those items of highest utility, those that Jane might be most likely to purchase. This, of course, is beneficial to Jane as well as the e-commerce store owner. Recommender systems research encompasses scenarios like this and many other information access environments in which a user and store owner can benefit from the presentation of personalized options. The field has seen a tremendous expansion of interest in the past decade, catalyzed in part by the Netflix Prize (Bennett and Lanning 2007) and evidenced by the rapid growth of the annual Association for Computing Machinery (ACM) Recommender Systems conference. At this point, it is worthwhile to take stock, to consider what distinguishes recommender systems research from other related areas of research in artificial intelligence, and to examine the field’s successes and new challenges.