As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by 'watching over the user's shoulder' while they browse. We show that these automatically created profiles reflect the user's interests quite well and they are able to produce moderate improvements when applied to search results.

Ontology-based personalized search and browsing

With the exponentially growing amount of information available on the Internet, the task of retrieving documents of interest has become increasingly difficult. Search engines usually return more than 1,500 results per query, yet out of the top twenty results, only one half turn out to be relevant to the user. One reason for this is that Web queries are in general very short and give an incomplete specification of individual users' information needs. This paper explores ways of incorporating users' interests into the search process to improve the results. The user profiles are structured as a concept hierarchy of 4,400 nodes. These are populated by 'watching over a user's shoulder' while he is surfing. No explicit feedback is necessary. The profiles are shown to converge and to reflect the actual interests quite well. One possible deployment of the profiles is investigated: re-ranking and filtering search results. Increases in performance are moderate but noticeable and show that fully automatic creation of large hierarchical user profiles is possible.

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Ontology based personalized search

Personalized Web browsing and search hope to provide Web information that matches a user's personal interests and thus provide more effective and efficient information access. A key feature in developing successful personalized Web applications is to build user profiles that accurately represent a user's interests. The main goal of this research is to investigate techniques that implicitly build ontology-based user profiles. We build the profiles without user interaction, automatically monitoring the user's browsing habits. After building the initial profile from visited Web pages, we investigate techniques to improve the accuracy of the user profile. In particular, we focus on how quickly we can achieve profile stability, how to identify the most important concepts, the effect of depth in the concept-hierarchy on the importance of a concept, and how many levels from the hierarchy should be used to represent the user.

Our major findings are that ranking the concepts in the profiles by number of documents assigned to them rather than by accumulated weights provides better profile accuracy. We are also able to identify stable concepts in the profile, thus allowing us to detect long-term user interests. We found that the accuracy of concept detection decreases as we descend them in the concept hierarchy, however this loss of accuracy must be balanced against the detailed view of the user available only through the inclusion of lower-level concepts.

Improving ontology-based user profiles

Recent advances in graph-based search techniques derived from Kleinberg's (1997) work have been impressive. This paper further improves the graph-based search algorithm in two dimensions. Firstly, variants of Kleinberg's techniques do not take into account the semantics of the query string nor of the nodes being searched. As a result, polysemy of query words cannot be resolved. This paper presents an interactive query scheme utilizing the simple Web taxonomy provided by the Open Directory Project to resolve meanings of a user query. Secondly, we extend a recently proposed personalized version of the Kleinberg algorithm (Chang et al., 2000). Simulation results are presented to illustrate the sensitivity of our technique. We outline the implementation of our algorithm in the Persona personalized web search system.

Persona: a contextualized and personalized web search

A method of creating user-experiential media services in an information system including several databases for information content, users of information services, information content parameters and user profile parameters, and a rulebase for the reaction impulses of a test user group to information stimuli presented. In the system, a database descriptive of users' reactions is created. Metadata files are created for the user of the information service and the content of the information service by comparing the actual parameters descriptive of the user and the content of the information service with the theoretical parameters included in the rulebase. Possible reactions of the interaction of the user and the content of the information service are specified, whereupon the desired reaction is selected among the possible reactions. The content of the information service is modified for presentation to the user in order to achieve the desired reaction.

User-specific personalization of information services

In the SmartPush project professional editors add semantic metadata to information flow when the content is created. This metadata is used to filter the information flow to provide the end users with a personalized news service. Personalization and delivery process is modeled as software agents, to whom the user delegates the task of sifting through incoming information. The key components of the SmartPush architecture have been implemented, and the focus in the project is shifting towards a pilot implementation and testing the ideas in practice.

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Teppo Kurki

Papers

Agents in Delivering Personalized Content Based on Semantic Metadata