Mining Indirect Association Rules for Web Recommendation

Kazienko, Przemysław

doi:10.2478/v10006-009-0015-5

Cited by 35 publications

(22 citation statements)

References 44 publications

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“…They are ideal for recommending resources related to another one, as they disregard the ordering relation between items. For this reason, numerous works have investigated the functionality of AR variations [2,20,13,18,25]. For example, a recent work by Kazienko explores indirect AR for web recommendations, involving resources that are not "hardly" connected as in typical AR [18].…”

Section: Revisitation Predictionmentioning

confidence: 99%

Client- and server-side revisitation prediction with SUPRA

Papadakis

Kawase

Herder

2012

Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics

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Users of collaborative applications as well as individual users in their private environment return to previously visited Web pages for various reasons; apart from pages visited due to backtracking, they typically have a number of favorite or important pages that they monitor or tasks that reoccur on an infrequent basis. In this paper, we introduce a library of methods that facilitate revisitation through the effective prediction of the next page request. It is based on a generic framework that inherently incorporates contextual information, handling uniformly both server-and the client-side applications. Unlike other existing approaches, the methods it encompasses are real-time, since they do not rely on training data or machine learning algorithms. We evaluate them over two large, real-world datasets, with the outcomes suggesting a significant improvement over methods typically used in this context. We have also made our implementation and data publicly available, thus encouraging other researchers to use it as a benchmark and to extend it with new techniques for supporting user's navigational activity.

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Section: Revisitation Predictionmentioning

confidence: 99%

Client- and server-side revisitation prediction with SUPRA

Papadakis

Kawase

Herder

2012

Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics

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“…This feature turns them ideal for recommending resources related to a particular site. Numerous works have investigated the functionality of different variations of AR [7,12,16]. For example, a recent work by Kazienko [12] explores indirect AR for Web recommendations, involving resources that are not "hardly" connected as in typical AR.…”

Section: Related Workmentioning

confidence: 99%

A Layered Approach to Revisitation Prediction

Papadakis

Kawase

Herder

et al. 2011

Lecture Notes in Computer Science

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Abstract. Web browser users return to Web pages for various reasons. Apart from pages visited due to backtracking, they typically have a number of favorite/important pages that they monitor or tasks that reoccur on an infrequent basis. In this paper, we introduce the architecture of a system that facilitates revisitations through the effective prediction of the next page request. It consists of three layers, each dealing with a specific aspect of revisitation patterns: the first one estimates the value of each page by balancing the recency and the frequency of its requests; the second one captures the contextual regularities in users' navigational activity in order to promote related pages, and the third one dynamically adapts the page associations of the second layer to the constant drift in the interests of users. For each layer, we introduce several methods, and evaluate them over a large, real-world dataset. The outcomes of our experimental evaluation suggest a significant improvement over other methods typically used in this context.

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“…Web usage mining, the art of analyzing user interactions with a web page, has been dealt by several researchers using different approaches. Association rule mining method ( [4], [5]) generates rule describing the relationship between user access and a web page. Though very successful, the number of association rules generated is often very huge and are often difficult to process and analyze which are being used to mine web log files.…”

Section: Related Workmentioning

confidence: 99%

Improve Efficiency of Web Pattern Analysis Through Web Mining

Kumawat¹,

Jain²

2014

IJCA

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In commercial and academic areas of Web usage mining techniques, the interest has been increased due to the information explosion in World Wide Web. Through web user navigation patterns, proposed a approach which predict users' future request that provide valuable information for web designers which quickly respond to their individual needs for the efficient organization of the website by mining the weblog files using Web weblog Expert and Universal Extractor. The approach is based on the combined mining to discover knowledge from web server log files which derive user navigation profiles and the contents of the retrieved web pages. The proposed algorithm implemented, and the system achieves the bandwidth of nearly 80% and the efficiency of about 75% of extracted log files, which is about 20% higher than original log files by mining Web server logs. This approach has been carried out in order to validate and facilitate better web personalization and website organization.

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Mining Indirect Association Rules for Web Recommendation

Cited by 35 publications

References 44 publications

Client- and server-side revisitation prediction with SUPRA

Client- and server-side revisitation prediction with SUPRA

A Layered Approach to Revisitation Prediction

Improve Efficiency of Web Pattern Analysis Through Web Mining

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