Recommender Systems

Abstract

Recommender systems are web-based applications that aim at helping customers in the decision making and product selection process (Resnick & Varian, 1997). The most prominent example is the online bookstore amazon.com, where collaborative filtering techniques are used to exploit similarities in the user profile which is based on the navigation and buying history: The main idea is to identify users who presumably have similar preferences and recommend those items which were bought by other users with a similar interest profile. Another technical approach is content-based filtering which builds on the hypothesis that the preferred items of a single user can be extrapolated from her preferences in the past, thus recommending "more of the same". The third principle approach is to use deep domain knowledge and to base the recommendations on a thorough understanding of the user’s current needs, comparable to real-life sales situation. Knowledge-based recommender systems elicit user preferences explicitly, i.e. they provide dynamic personalized and potentially persuasive sales dialogues (Burke, 2000), (Jannach, 2004). The recommendations are the result of a reasoning process on the domain knowledge which also forms the basis for explaining to the user why an item is proposed. Furthermore, hybrid approaches (Burke, 2002) are used to overcome some of the shortcomings such as cold start problems for new users and new products when using collaborative filtering, lack of serendipity for content based filtering applications or a high knowledge acquisition effort when employing an explicit model of the domain (Adomavicius & Tuzhilin, 2005).