The Adaptive Web Methods and Strategies of Web Personalization
Following the increase in of the information available on the Web, the diversity of its users and the complexity of Web applications, researchers started developing adaptive Web systems that tailored their appearance and behavior to each individual u
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Rutgers University, ASBIII, 3 Rutgers Plaza New Brunswick, NJ 08901 [email protected] 2 FX Palo Alto Laboratory, Inc., 3400 Hillview Ave, Bldg. 4 Palo Alto, CA 94304 [email protected]
Abstract. This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.
10.1
Introduction
A common scenario for modern recommendation systems is a Web application with which a user interacts. Typically, a system presents a summary list of items to a user, and the user selects among the items to receive more details on an item or to interact with the item in some way. For example, online news sites present web pages with headlines (and occasionally story summaries) and allow the user to select a headline to read a story. E-commerce sites often present a page with a list of individual products and then allow the user to see more details about a selected product and purchase the product. Although the web server transmits HTML and the user sees a web page, the web server typically has a database of items and dynamically constructs web pages with a list of items. Because there are often many more items available in a database than would easily fit on a web page, it is necessary to select a subset of items to display to the user or to determine an order in which to display the items. Content-based recommendation systems analyze item descriptions to identify items that are of particular interest to the user. Because the details of recommendation systems differ based on the representation of items, this chapter first discusses alternative item representations. Next, recommendation algorithms suited for each representation are discussed. The chapter concludes with a discussion of variants of the approaches, P. Brusilovsky, A. Kobsa, and W. Nejdl (Eds.): The Adaptive Web, LNCS 4321, pp. 325 – 341, 2007. © Springer-Verlag Berlin Heidelberg 2007
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M.J. Pazzani and D. Billsus
the strengths and weaknesses of content-based recommendation systems, and directions for future research and development. 10.1.1
Item Representation
Items that can be recommended to the user are often stored in a database table. Table 10.1 shows a simple database with records (i.e., “rows”) that describe three restaurants. The column names (e.g., Cuisine or Service) are properties of restaurants
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