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Title
Abstract
Introduction
Methods for User Identification
Methods for User Information Collection
Conclusions
References
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Computer Science
Human-Computer Interaction

User Profiles for Personalized Information Access

Profile image of Mirco SperettaMirco Speretta

2007, Lecture Notes in Computer Science

https://doi.org/10.1007/978-3-540-72079-9_2
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Abstract

The amount of information available online is increasing exponentially. While this information is a valuable resource, its sheer volume limits its value. Many research projects and companies are exploring the use of personalized applications that manage this deluge by tailoring the information presented to individual users. These applications all need to gather, and exploit, some information about individuals in order to be effective. This area is broadly called user profiling. This chapter surveys some of the most popular techniques for collecting information about users, representing, and building user profiles. In particular, explicit information techniques are contrasted with implicitly collected user information using browser caches, proxy servers, browser agents, desktop agents, and search logs. We discuss in detail user profiles represented as weighted keywords, semantic networks, and weighted concepts. We review how each of these profiles is constructed and give examples of projects that employ each of these techniques. Finally, a brief discussion of the importance of privacy protection in profiling is presented. This chapter discusses user profiles specifically designed for providing personalized information access. Other types of profiles, build using different construction techniques, are described elsewhere in this book. In particular, Chapter 4 [40] discusses generic user modeling systems that are broader in scope, not necessarily focused on Internet applications. Related research on collaborative recommender systems, discussed in Chapter 9 of this book [81], combines information from multiple users in order to provide improved information services. Concern over privacy protection is growing in parallel with the demand for personalized features. These two trends seem to be in direct opposition to each other, so privacy protection must be a crucial component of every personalization system. A detailed discussion can be found in Chapter 21 of this book [39]. There are a wide variety of applications to which personalization can be applied and a wide variety of different devices available on which to deliver the personalized information. Early personalization research focused on personalized filtering and/or rating systems for e-mail [49], electronic newspapers [14, 16], Usenet newsgroups [41, 58, 86, 91, 106], and Web documents [4]. More recently, personalization efforts have focused on improving navigation effectiveness by providing browsing assistants [9, 13], and adaptive Web sites [69]. Because search is one of the most common activities performed today, many projects are now focusing on personalized Web search [46, 88, 92] and more details on the subject can be found in Chapter 6 of this book [52]. However, personalized approaches to searching other types of collections, e.g., short stories [76], Java source code [100], and images [14] have also been explored. Commercial products are also adopting personalized features, for example, Yahoo!'s personalized Web portals [110] and Google Lab's personalized search [30]. The aforementioned systems are just a few examples that illustrate the breadth of applications to which personalized approaches are being investigated. Nichols [63] and Oard and Marchionini [64] provide a general overview of some the issues and approaches to personalized rating and filtering and Pretschner [71] describes approximately 45 personalization systems. Most personalization systems are based on some type of user profile, a data instance of a user model that is applied to adaptive interactive systems. User profiles may include demographic information, e.g., name, age, country, education level, etc, and may also represent the interests or preferences of either a group of users or a single person. Personalization of Web portals, for example, may focus on individual users, for example, displaying news about specifically chosen topics or the market summary of specifically selected stocks, or a groups of users for whom distinctive characteristics where identified, for example, displaying targeted advertising on ecommerce sites. In order to construct an individual user's profile, information may be collected explicitly, through direct user intervention, or implicitly, through agents that monitor user activity. Although profiles are typically built only from topics of interest to the user, some projects have explored including information about non-relevant topics in the profile [35, 104]. In these approaches, the system is able to use both kinds of topics to identify relevant documents and discard non-relevant documents at the same time. Profiles that can be modified or augmented are considered dynamic, in contrast to static profiles that maintain the same information over time. Dynamic profiles that Explicit info Data Collection Technology Or Application Profile Constructor User Implicit info Keyword profile Semantic Net profile Concept profile Personalized Services As shown in Figure 2.1, the user profiling process generally consists of three main phases. First, an information collection process is used to gather raw information about the user. As described in Section 2.2, depending on the information collection process selected, different types of user data can be extracted. The second phase focuses on user profile construction from the user data. Section 2.3 summarizes a variety of ways in which profiles may be represented and Section 2.4 some of the ways a profile may be constructed. The final phase, in which a technology or application exploits information in the user profile in order to provide personalized services, is discussed in Parts II and III of this book. 2.2 Collecting Information About Users The first phase of a profiling technique collects information about individual users. A basic requirement of such a system is that it must be able to uniquely identify users. This task is described in more detail in Section 2.2.1. The information collected may be explicitly input by the user or implicitly gathered by a software agent. It may be collected on the user's client machine or gathered by the application server itself. Depending on how the information is collected, different data about the users may be extracted. Several options, and their impacts, are discussed in Section 2.2.2. In

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