Personalized Web Page Recommendation Using Ontology

In this network era, Web Page Recommendation and web page Recommendation systems can take advantage of semantic network reasoning-capabilities to overcome common limitations of current systems and improve the recommendations’ quality. This paper presents a personalized-web-recommendation system, a system that makes use of representations of items and user-profiles based on ontology in order to provide semantic applications with personalized services. The recommender uses domain ontology to enhance the personalization: on the other hand, user’s interests are modeled in a more effective and accurate way by applying a domain-based inference method; on the other hand, the stemmer algorithm used by our content-based filtering approach, which provides a measure of the affinity between an item and a user, is enhanced by applying a semantic similarity method. Web Usage Mining plays an important role in web page recommender systems and web personalization system. In this paper, we propose an effective personalized web recommendation system based on ontology and Web Usage Mining. The proposed approach integrates semantic knowledge into Web Usage Mining and personalization processes. DOI: 10.17762/ijritcc2321-8169.15071

Web-Page Recommendation Based on Web Usage and Domain Knowledge

© 1989-2012 IEEE. Web-page recommendation plays an important role in intelligent Web systems. Useful knowledge discovery from Web usage data and satisfactory knowledge representation for effective Web-page recommendations are crucial and challenging. This paper proposes a novel method to efficiently provide better Web-page recommendation through semantic-enhancement by integrating the domain and Web usage knowledge of a website. Two new models are proposed to represent the domain knowledge. The first model uses an ontology to represent the domain knowledge. The second model uses one automatically generated semantic network to represent domain terms, Web-pages, and the relations between them. Another new model, the conceptual prediction model, is proposed to automatically generate a semantic network of the semantic Web usage knowledge, which is the integration of domain knowledge and Web usage knowledge. A number of effective queries have been developed to query about these knowledge bases. Based on these queries, a set of recommendation strategies have been proposed to generate Web-page candidates. The recommendation results have been compared with the results obtained from an advanced existing Web Usage Mining (WUM) method. The experimental results demonstrate that the proposed method produces significantly higher performance than the WUM method

A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table

SemAware: An Ontology-Based Web Recommendation System

Web Recommendation Systems (WRS\u27s) are used to recommend items and future page views to world wide web users. Web usage mining lays the platform for WRS\u27s, as results of mining user browsing patterns are used for recommendation and prediction. Existing WRS\u27s are still limited by several problems, some of which are the problem of recommending items to a new user whose browsing history is not available (Cold Start), sparse data structures (Sparsity), and no diversity in the set of recommended items (Content Overspecialization). Existing WRS\u27s also fail to make full use of the semantic information about items and the relations (e.g., is-a, has-a, part-of) among them. A domain ontology, advocated by the Semantic Web, provides a formal representation of domain knowledge with relations, concepts and axioms.This thesis proposes SemAware system, which integrates domain ontology into web usage mining and web recommendation, and increases the effectiveness and efficiency of the system by solving problems of cold start, sparsity, content overspecialization and complexity-accuracy tradeoffs. SemAware technique includes enriching the web log with semantic information through a proposed semantic distance measure based on Jaccard coefficient. A matrix of semantic distances is then used in Semantics-aware Sequential Pattern Mining (SPM) of the web log, and is also integrated with the transition probability matrix of Markov models built from the web log. In the recommendation phase, the proposed SPM and Markov models are used to add interpretability. The proposed recommendation engine uses vector-space model to build anitem-concept correlation matrix in combination with user-provided tags to generate top-n recommendation.Experimental studies show that SemAware outperforms popular recommendation algorithms, and that its proposed components are effective and efficient for solving the contradicting predictions problem, the scalability and sparsity of SPM and top-n recommendations, and content overspecialization problems

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure