NO MUDDIST FOR the class on 03/31
As the information on the internet is increasing continuously, personalized approach is very needed to give each user their unique information access regarding their interest, browsing history, and etc.
The first step is to collecting information about users. A basic requirement of such a system is that it must be able to uniquely identify users. Although accurate user identification is not a critical issue for systems that construct profiles representing groups of users, it is a crucial ability for any system that constructs profiles that represent individual users. There are five basic approaches to user identification: software agents, logins, enhanced proxy servers, cookies, and session ids.
As the information on the internet is increasing continuously, personalized approach is very needed to give each user their unique information access regarding their interest, browsing history, and etc.
The first step is to collecting information about users. A basic requirement of such a system is that it must be able to uniquely identify users. Although accurate user identification is not a critical issue for systems that construct profiles representing groups of users, it is a crucial ability for any system that constructs profiles that represent individual users. There are five basic approaches to user identification: software agents, logins, enhanced proxy servers, cookies, and session ids.
The second step is user profile representations. The most common representation for user profiles is sets of keywords. These can be automatically extracted from Web documents or directly provided by the user. Weights, which are usually associated with keywords, are numerical representations of user’s interests. In order to address the polysemy problem inherent with keyword-based profiles, the profiles may be represented by a weighted semantic network in which each node represents a concept. Concept-based profiles are similar to semantic network-based profile in the sense that both are represented by conceptual nodes and relationships between those nodes.
The third step is user profile construction. Keyword-based profiles are initially created by extracting keywords from Web pages collected from some information source, e.g., the user’s browsing history or bookmarks. Semantic network-based profiles are typically built by collecting explicit positive and/or negative feedback from users. Similar to keyword vector profile construction techniques, keywords are extracted from the user-rated pages. This section describes three representative systems that build user profiles represented as weighted concept hierarchies. Although each uses a different construction methodology, they each use a reference taxonomy as the basis of the profile.
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