Multi-agent Web Recommendation Method Based on Indirect Association Rules

Kazienko, Przemysław

doi:10.1007/978-3-540-30133-2_154

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…The recommender system based on association rules was implemented with a distributed architecture (Kazienko, 2004a). Each system module may be treated as a software expert-agent that possesses its own characteristic depending on its role in the recommendation process (Fig.…”

Section: Architecture Of the Recommender Systemmentioning

confidence: 99%

Mining Indirect Association Rules for Web Recommendation

Kazienko¹

2009

International Journal of Applied Mathematics and Computer Science

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Classical association rules, here called "direct", reflect relationships existing between items that relatively often co-occur in common transactions. In the web domain, items correspond to pages and transactions to user sessions. The main idea of the new approach presented is to discover indirect associations existing between pages that rarely occur together but there are other, "third" pages, called transitive, with which they appear relatively frequently. Two types of indirect associations rules are described in the paper: partial indirect associations and complete ones. The former respect single transitive pages, while the latter cover all existing transitive pages. The presented IDARM* Algorithm extracts complete indirect association rules with their important measure-confidence-using pre-calculated direct rules. Both direct and indirect rules are joined into one set of complex association rules, which may be used for the recommendation of web pages. Performed experiments revealed the usefulness of indirect rules for the extension of a typical recommendation list. They also deliver new knowledge not available to direct ones. The relation between ranking lists created on the basis of direct association rules as well as hyperlinks existing on web pages is also examined.

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Section: Architecture Of the Recommender Systemmentioning

confidence: 99%

Mining Indirect Association Rules for Web Recommendation

Kazienko¹

2009

International Journal of Applied Mathematics and Computer Science

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“…At present there are various personalized advertisement recommendation models [3], [5], [7], [10], [12]. Furthermore, they are gradually applied nowadays.…”

Section: Personalized Advertisement Recommendation Modelmentioning

confidence: 99%

Study on Personalized Recommendation Model of Internet Advertisement

Zhou

Chen

Zhang

IFIP International Federation for Information Processing

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Abstract. With the rapid development of E-Commerce, the audiences put forward higher requirements on personalized Internet advertisement than before. The main function of Personalized Advertising System is to provide the most suitable advertisements for anonymous users on Web sites. The paper offers a personalized Internet advertisement recommendation model. By mining the audiences' historical and current behavior, and the advertisers' and publisher's web site content, etc, the system can recommend appropriate advertisements to corresponding audiences.

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“…Experimental results show that the proposed approach performs much better recommendation than traditional collaborative recommendation approaches. Kazienko[Kaz04] proposed an approach to combine association rules within and between web user sessions into complex association rules to compute ranking lists of web pages for recommendation. All recommendation tasks are distributed between many agents that communicate their knowledge with each other.…”

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confidence: 99%

Web usage mining for web personalization

Zhou¹

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With the explosive growth of information available on the World Wide Web, it has become much more difficult to access relevant information from the Web. Various web services have been developed to assist users' web browsing activities. However, the existing web services provided are far from satisfactory to support the needs of different users. One possible solution to solve this problem is web personalization, which aims to customize the content and structure of a website to the needs of specific users by taking advantage of the knowledge acquired from the analysis of users' access behaviors. Discovering and understanding users' web access behaviors, especially surfing interests and habits, are very important for supporting effective web personalization. Web usage mining is a promising approach for discovering users' web access patterns from web usage logs. The objective of this research is to investigate novel web usage mining approaches for discovering users' web access patterns and effective techniques for web personalization. This research aims to investigate techniques for enhancing web usage mining in both access pattern representation and mining algorithms, and techniques for applying the discovered access patterns to support effective personalized services on the Web and Semantic Web. The contributions of this research are listed as follows: • An efficient sequential pattern mining algorithm, called CSB-mine (Conditional Sequence Base mining algorithm), is proposed for discovering frequent sequential patterns efficiently from sequence databases, especially the web usage logs. • A web recommender system, named SWARS (Sequential Web Access based Recommender System), is developed for matching a user's current access sequence efficiently and recommending related web pages to the user effectively based on the sequential access patterns mined by the proposed CSB-mine algorithm. • A web usage mining approach is proposed for discovering a specific kind of periodic association access patterns of individual users from web usage logs. The proposed approach incorporates fuzzy set theory into Formal Concept Analysis (FCA) for constructing a i ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library novel user behavior model, called Personal Web Usage Lattice, from which periodic association access patterns of the user can be extracted and visualized. • A Personal Web Usage Lattice based approach is proposed for periodic web personalization. Different from non-periodic approaches, the proposed periodic web personalization approach can determine efficiently which resources a user is most probably interested in during a given time period based on the Personal Web Usage Lattice of the user, without the use of the user's current access information. This makes it possible to perform more costly personalized resource preparation in advance rather than in real-time for periodic web personalization. • A semantic web personalization framework is proposed. In the propo...

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Multi-agent Web Recommendation Method Based on Indirect Association Rules

Cited by 11 publications

References 12 publications

Mining Indirect Association Rules for Web Recommendation

Mining Indirect Association Rules for Web Recommendation

Study on Personalized Recommendation Model of Internet Advertisement

Web usage mining for web personalization

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