A multi-criteria content-based filtering system

Pasi, Gabriella; Bordogna, Gloria; Villa, Robert

doi:10.1145/1277741.1277903

Cited by 19 publications

(14 citation statements)

References 3 publications

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“…These systems primarily use only the weighted sum (i.e., linear combination) model. Of the information filtering systems we are aware of, PENG [11] is the most similar in application to our experimental domain. PENG is a multi-criteria news bulletin filtering system that utilizes several criteria, including content, coverage, reliability, novelty and timeliness.…”

Section: Related Workmentioning

confidence: 99%

Interaction and Personalization of Criteria in Recommender Systems

Wolfe

Zhang

2010

User Modeling, Adaptation, and Personalization

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Abstract. A user's informational need and preferences can be modeled by criteria, which in turn can be used to prioritize candidate results and produce a ranked list. We examine the use of such a criteria-based user model separately in two representative recommendation tasks: news article recommendations and product recommendations. We ask the following: are there nonlinear interactions among the criteria; and should the models be personalized? We assume that that user ratings on each criterion are available, and use machine learning to infer a user model that combines these multiple ratings into a single overall rating. We found that the ratings of different criteria have a nonlinear interaction in some cases, for example, article novelty and subject relevance often interact. We also found that these interactions vary from user to user.

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Section: Related Workmentioning

confidence: 99%

Interaction and Personalization of Criteria in Recommender Systems

Wolfe

Zhang

2010

User Modeling, Adaptation, and Personalization

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“…Content-based filtering (CBF) has been used mainly in the context of recommending items such as web pages and news articles, etc, by analyzing their content descriptions [16]. The content is parsed, and item features are extracted to establish a characteristic profile.…”

Section: Recommender Systemsmentioning

confidence: 99%

Combining reputation and content-based filtering for blog article recommendation in social bookmarking websites

Peng

Liu

2010

Proceedings of the 12th International Conference on Electronic Commerce: Roadmap for the Future of Electronic Business

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The new generation of web-based communities, Web2.0, represents an innovative spirit in sharing and managing contents. Social bookmarking is a portal for users to share, organize, search, and manage bookmarks of web resources. However, with the rapid growth of web documents that are produced every day, people are facing the problem of information overload. The Social bookmarking web site provides the push (user recommendation) counts of articles indicating the recommended popularity degrees of articles. In this paper, we propose to derive the popularity degree of an article by considering the reputation of users that push the article. Moreover, we propose a personalized blog article recommendation approach, which combines the reputation-based popularity with content based filtering, to recommend popular blog articles to users that satisfy their personal preferences. Our experimental results show that the proposed approach outperforms conventional approaches.

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“…Within the PENG project (Pasi et al 2007;Pasi and Villa 2005), a multi criteria filtering model was defined and implemented. This is a content-based filtering model based on the consideration of five distinct properties that can be measured by the filtering system to an incoming document in order to assess its relevance to a user profile.…”

Section: Content-based Information Filteringmentioning

confidence: 99%

“…Within the PENG (PErsonalized News content programminG) project, a Specific Targeted Research project (STREP) funded by the European Community within the Sixth Framework Programme (Pasi et al 2007;Pasi and Villa 2005), a first version of a flexible content-based multi criteria filtering model was defined and implemented. To estimate the relevance of incoming documents, the filter combines the evaluations of several content-based criteria such as document aboutness, i.e., content similarity, coverage, i.e., completeness of document contents with respect to user interests, novelty of contents with respect to already seen documents, and other criteria such as timeliness, and trust in the information sources.…”

Section: Introductionmentioning

confidence: 99%

A flexible multi criteria information filtering model

Bordogna

Pasi

2009

Soft Comput

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In this paper, a flexible multi criteria information filtering model is presented. This model is flexible since it allows choosing several distinct criteria, such as content aboutness, coverage, novelty, trust, timeliness, and combining them by a soft aggregation to define a personalized filter. The personal filter is encoded into the user profile that also contains the representations of the user interests that can evolve over time. An implementation of the system applying a combination of the aboutness and coverage criteria has been evaluated and compared to other filtering systems, showing its superior effectiveness. Finally, the possible use of the other criterion is discussed.

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A multi-criteria content-based filtering system

Cited by 19 publications

References 3 publications

Interaction and Personalization of Criteria in Recommender Systems

Interaction and Personalization of Criteria in Recommender Systems

Combining reputation and content-based filtering for blog article recommendation in social bookmarking websites

A flexible multi criteria information filtering model

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