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Delivering Personalized Information: What You Get Is What You Want

The information age has brought with it massive amounts of data and the promise of being able to access any information when needed. However, the tools to help us navigate through this maze of information are not well developed. Users are faced with a significant information overload problem as they become more and more frustrated by their inability to locate the right information at the right time. Personalized recommendation systems have been forwarded as a potential solution. These systems customize themselves to their respective user by learning about their user’s preferences and process, select and display information accordingly. In this paper we survey some of the principal personalization techniques in the context of a number of case-studies. 1. The Need for Personalization As information becomes abundant, humans are confronted with more difficult decisions for how to access, navigate through and select available options. The sheer number of alternatives often makes a wise choice impossible without some intelligent computational assistance. The term information overload has become almost synonymous with the Internet. In response to this need, there have been increased efforts to design and implement intelligent aides for filtering web sites (e.g., Pazzani, Muramatsu, and Billsus (1996)), news stories (e.g., Lang (1995)), TV listing (Smyth and Cotter, (1999, 2000), Cotter and Smyth (2000a&b)) and other information sources. A related line of research and development has led to recommendation systems (e.g. Burke, Hammond, and Young (1996), Resnick and Varian (1997), Burke (1999)), which are not limited to filtering information but can be used for any task that requires choice among a large set of predefined items. Given a large set of items and a description of the user's needs, recommendation systems present to the user a small set of the items that are suited to these requirements. Society, on the other hand, is getting more complex and diversified. The differences in personal preferences, social and educational backgrounds and private or professional interests are increasing, and tools to access information are becoming ubiquitous. This causes the need for intelligent systems that process, filter and display available information in a manner suitable for the characteristics and desires of each of these individuals. The ‘one result fits all’ approach to item selection delivered by standard recommendation systems does not satisfy this need. Research on personalization has led to the development of systems that adapt themselves to the characteristics of their user: user adaptive or personalized systems (e.g. Rich (1979), Langley (1997), Ardissono, Barbero, Goy, and Petrone (1999), Ferrario, Waters and Smyth (2000), McGinty and Smyth (2000), Fiechter and Rogers (2000)). 2. Personalized Systems Raw data usually does not change based on the individual processing it. However, the resulting information and the manner in which it is presented can be influenced by personal differences. A computer system should ultimately be sophisticated enough to take individual variations in preferences, goals, and backgrounds into account and generate, select, and present personalized information. Personalized systems obtain user preferences trough interactions with users, summarize these preferences in a user model and utilize this model to adapt themselves to generate customized information or behavior (see Fig. 1). They deliver customized information in the manner that is most desirable for the current user, thereby increasing the quality of both the interaction and the generated result.