A Semantically Enriched Web Usage Based Recommendation Model

Abstract

With the rapid growth of internet technologies, Web has become a huge repository of information and keeps growing exponentially under no editorial control. However the human capability to read, access and understand Web content remains constant. This motivated researchers to provide Web personalized online services such as Web recommendations to alleviate the information overload problem and provide tailored Web experiences to the Web users. Recent studies show that Web usage mining has emerged as a popular approach in providing Web personalization. However conventional Web usage based recommender systems are limited in their ability to use the domain knowledge of the Web application. The focus is only on Web usage data. As a consequence the quality of the discovered patterns is low. In this paper, we propose a novel framework integrating semantic information in the Web usage mining process. Sequential Pattern Mining technique is applied over the semantic space to discover the frequent sequential patterns. The frequent navigational patterns are extracted in the form of Ontology instances instead of Web page views and the resultant semantic patterns are used for generating Web page recommendations to the user. Experimental results shown are promising and proved that incorporating semantic information into Web usage mining process can provide us with more interesting patterns which consequently make the recommendation system more functional, smarter and comprehensive.

Webpage Recommendation System Based on the Social Media Semantic Details of the Website

“…But this system failed to concentrate on web usage knowledge base update, multi-site, domain knowledge discovery, web usage data and representation for web page recommendation. A semantically enriched web usage based recommendation model (Ramesh, C. et al 2011) combined semantic information with web usage mining process. The sequential pattern mining technique was applied in the semantic space to find out the frequent sequential patterns.…”

Using Domain Ontology and Sequential Rule Mining for Extracting Behavior Patterns from Web Navigation Logs

“…In a recent work [13], Nasraoui et al, proposed a Web Usage Mining Framework for mining evolving User Profiles of dynamic Web sites by exploiting the external ontology, used for mapping and relating dynamic Web pages. Eirinaki et al [3], presented a system, SEWeP which integrates the Web usage logs with the semantics of Web site's content to improve the personalization. The innovative feature of the architecture was C-logs, an extended form of Web usage log which encapsulates the site semantics.…”

“…As a consequence, the quality of the discovered patterns is low. These patterns do not provide explicit insight into the user's underlying interests and preferences, thus limiting the effectiveness of recommendations as well as the ability of the system to interpret and explain the recommendations [3].…”

Full Data Controlled Web-Based Feed Aggregator

“…The database recommendation is one of the fundamental aims of this project. With the rapid growth of internet technologies [2,17,21] the social web is one other main feature lacking in most readers; this technology can aid research and information sharing when carefully integrated [1,2,20,21,22].…”