@article {101,
	title = {Personalized searching by learning wordnet-based user profiles},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2007},
	pages = {309 - 322},
	abstract = {The amount of information available on the Web and in Digital Libraries is increasing over time. In this context, the role of user modeling and personalized information access is becoming crucial: Users need a personalized support in sifting through large amounts of retrieved information according to their interests. Information filtering and retrieval systems relying on this idea adapt their behavior to individual users by learning their preferences during the interaction in order to construct a profile of the user that can be later exploited in the search process. We propose a novel technique to learn user profiles which exploits word sense disambiguation based on the WordNet lexical database, in an attempt to produce semantic user profiles that might discover topics semantically closer to the user interests. Semantic profiles are used in the definition of a retrieval model that turns the traditional document-query search paradigm into a novel document-query-profile paradigm. As an example of this paradigm, we present an extension of the vector space model in which profiles are used to modify the ranking of search results obtained in response to a query, hopefully putting personally relevant items on the top of the result list. Experimental results in a movie retrieval scenario indicate that the proposed model to personalize Web search is effective.},
	keywords = {Machine learning, Personalization and information filtering, User modeling, WordNet},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-66149191527\&partnerID=40\&md5=750d800edd5f7fe76d849dd465dafff9},
	author = {Semeraro, G.}
}
@article {54,
	title = {Study of effectiveness of implicit indicators and their optimal combination for accurate inference of users interests},
	journal = {Journal of Digital Information Management},
	volume = {4},
	year = {2006},
	note = {Export Date: 10 July 2014},
	month = {2006},
	pages = {169 - 174},
	abstract = {Retrieval and filtering systems may apply relevance feedback to gain information on users{\textquoteright} needs in order to improve their ad-hoc queries or long term profiling. Explicit relevance involves explicit ratings of documents or terms by the users and disrupts their normal patterns of browsing and searching. The alternative non-disruptive method is implicit feedback inferring users{\textquoteright} needs and interests by monitoring their regular interaction with the system. Some implicit indicators of interest, such as reading time, have been investigated in previous studies and were found indicative to the relevance of documents but not sufficiently accurate to substitute explicit ratings. In this paper we present several new relative implicit feedback indicators and examine their effectiveness as well as the effect of combining several implicit indicators. The paper describes a large-scale user study on which users{\textquoteright} searches were observed by a specially developed browser that recorded their behavior (implicit indicators) as well as their explicit ratings. The relationship between implicit indicators and explicit ratings was analyzed and found that a certain combination of implicit indicators achieved higher correlation with the explicit ratings than any of the individual indicators. We have also found that the newly suggested relative indicators are more indicative to the level of interest of the use in an information item than the non-relative indicators.},
	keywords = {Implicit indicators, Relevance feedback, User modeling, User studies},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749406365\&partnerID=40\&md5=aa35b421ac892afda1341ff1d8c1ae20},
	author = {Shapira, B. and Taieb-Maimon, M. and Moskowitz, A.}
}
