ersonalization technologies and recommender systems help online consumers avoid information overload by making suggestions regarding which information is most relevant to them. Most online shopping sites and many other applications now use recommender systems. The most popular examples include Netflix, which recommends movies, and Amazon. com, which recommends books, CDs, and various other products. Users offer feedback on purchased or consumed items, and the recommender system uses the information to predict their preferences for yet unseen items and subsequently recommends items with the highest predicted relevance. The vast majority of current recommender systems use a single criterion, such as a single numerical rating, to represent an item's utility to a user in a 2D UsersÍ tems space. Single-criterion rating systems have proved successful in several applications, but many industries have begun employing multicriteria systems. For example, restaurant guides, such as Zagat's Guide, provide three criteria for restaurant ratings (food, décor, and service). Online shopping malls, such as Circuitcity.com and Buy.com, use multicriteria ratings for consumer electronics (display, performance, battery life, and cost). However, these rating systems are not used in the context of personalization; the rating on each criterion-for example, Zagat's "food" rating for a specific restaurant-is the same for all users. In contrast, Yahoo! Movies recently launched a recommendation service that employs user-specific multicriteria ratings for each movie. This move indicates that multicriteria data provides value to online content providers and consumers as a component in personalization applications. Taking full advantage of multicriteria ratings in personalization applications requires new recommendation techniques. In this article, we propose several new techniques for extending recommendation technologies to incorporate and leverage multicriteria rating information (see the sidebar for related work). Recommender systems are usually classified according to their recommendation approach: 1 • Content-based approaches recommend items similar to those the user preferred in the past. • Collaborative-filtering approaches recommend items that users with similar preferences have liked in the past. • Hybrid approaches combine content-based and collaborative-filtering methods in several different ways. 2 We can also classify recommender systems according to their algorithmic technique: 3 • Memory-based techniques usually represent heuristics that calculate recommendations on the fly based directly on previous user activities. • Model-based techniques use previous user activities to first learn a predictive model (typically using some statistical or machine-learning method), which the system then uses to make recommendations. A comprehensive survey of recommender systems research literature is beyond the scope of this article, although a recent survey is available elsewhere. 2 Typically, the recommendation process starts with the specification of an initial ratings set, which either the user explicitly provides or the system implicitly infers. For example, in a movie recommender sys-
The most popular recommendation approaches are REF : (1) content-based filtering, (2) collaborative filtering and (3) hybrid.
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
IEEE Intelligent Systems
Computer Science