Algorithmic Aspects of Web Intelligent Systems

Kalles, Dimitris; Papagelis, Athanasios; Zaroliagis, Christos

doi:10.1007/978-3-662-05320-1_15

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…The two different approaches are content based (Balabanovic & Sholam, 1997;Kalles, Papagelis, & Zaroliagis, 2003) and collaborative filtering (Herlocker, Konstan, Terveen, & Riedl, 2004;Hofmann, 2003), while additional hybrid techniques have been proposed as well (Balabanovic & Sholam, 1997).…”

Section: Case Study: Complementary-based Recommender Systemmentioning

confidence: 99%

A semantic complement to enhance electronic market

Moosavi

Nematbakhsh

Farsani

2009

Expert Systems with Applications

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Section: Case Study: Complementary-based Recommender Systemmentioning

confidence: 99%

A semantic complement to enhance electronic market

Moosavi

Nematbakhsh

Farsani

2009

Expert Systems with Applications

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“…The two different approaches are content-based (Balabanovic and Sholam, 1997;Kalles et al, 2003) and collaborative filtering (Herlocker et al, 2000;Hofmann, 2003), while additional hybrid techniques have been proposed as well (Balabanovic and Sholam, 1997).…”

Section: Approaches Of Recommendation Algorithmsmentioning

confidence: 99%

Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents

Papagelis

Plexousakis

2005

Engineering Applications of Artificial Intelligence

159

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Recommendation agents employ prediction algorithms to provide users with items that match their interests. In this paper, several prediction algorithms are described and evaluated, some of which are novel in that they combine user-based and item-based similarity measures derived from either explicit or implicit ratings. Both statistical and decision-support accuracy metrics of the algorithms are compared against different levels of data sparsity and different operational thresholds. The first metric evaluates the accuracy in terms of average absolute deviation, while the second evaluates how effectively predictions help users to select highquality items. The experimental results indicate better performance of item-based predictions derived from explicit ratings in relation to both metrics. Category-boosted predictions lead to slightly better predictions when combined with explicit ratings, while implicit ratings, in the context that have been defined in this paper, perform much worse than explicit ratings. r

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“…A content-based approach [6,23,31] provides recommendations by comparing an item's content with the content that the user is interested in. In this approach, a model of user ratings is first developed.…”

Section: Content-based Recommendationmentioning

confidence: 99%