User Profile Representation Research Articles

A structure-based approach for multimedia information filtering

While multimedia documents are sequentially presented to users, an information filtering (IF) system is useful to achieve a good retrieval performance in terms of both quality and efficiency. Conventional approaches for designing an IF system are based on the user's evaluation on information relevance degree (IRD), but ignore other attributes in system design such as relative importance of the data in a collection of multimedia documents. In this paper, we aim at developing a framework of designing structure-based multimedia IF systems, which incorporates the characteristics of the importance and relevance of multimedia documents. A method of calculating the values of relative importance degree of multimedia documents is proposed. Furthermore, these values are combined into the IRD of multimedia documents to improve the representation of user profiles. An illustrative example is given to demonstrate the proposed techniques.

Learning for automatic personalization in a semantic taxonomy-based meta-search agent

Providing highly relevant page hits to the user is a major concern in Web search. To accomplish this goal, the user must be allowed to express his intent precisely. Secondly, page hit rating mechanisms should be used that take the user’s intent into account. Finally, a learning mechanism is needed that captures a user’s preferences in his Web search, even when those preferences are changing dynamically. To address the first two issues, we propose a semantic taxonomy-based meta-search agent approach that incorporates the user’s taxonomic search intent. It also addresses relevancy improvement issues of the resulting page hits by using user’s search intent and preference-based rating. To provide a learning mechanism, we first propose a connectionist model-based user profile representation approach, which can leverage all of the features of the semantic taxonomy-based information retrieval approach. A user profile learning algorithm is also devised for our proposed user profile representation framework by significantly modifying and extending a typical neural network learning algorithm. Finally, the entire methodology including this learning mechanism is implemented in an agent-based system, WebSifter II. Empirical results of learning performance are also discussed.