Exploiting Synergy Between Ontologies and Recommender Systems

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured

Twelve Theses on Reactive Rules for the Web

Reactivity, the ability to detect and react to events, is an essential functionality in many information systems. In particular, Web systems such as online marketplaces, adaptive (e.g., recommender) systems, and Web services, react to events such as Web page updates or data posted to a server. This article investigates issues of relevance in designing high-level programming languages dedicated to reactivity on the Web. It presents twelve theses on features desirable for a language of reactive rules tuned to programming Web and Semantic Web applications

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

Semantic-enhanced web-page recommender systems

University of Technology, Sydney. Faculty of Engineering and Information Technology.This thesis presents a new framework for a semantic-enhanced Web-page recommender (WPR) system, and a suite of enabling techniques which include semantic network models of domain knowledge and Web usage knowledge, querying techniques, and Web-page recommendation strategies. The framework enables the system to automatically discover and construct the domain and Web usage knowledge bases, and to generate effective Webpage recommendations. The main contributions of the framework are fourfold: (1) it effectively changes the fact that knowledge base construction must rely on human experts; (2) it enriches the pool of candidate Web-pages for effective Web-page recommendations by using semantic knowledge of both Web-pages and Web usage; (3) it thoroughly resolves the inconsistency problem facing contemporary WPR systems which heavily employ heterogeneous representations of knowledge bases. Knowledge bases in the system are consistently represented in a formal Web ontology language, namely OWL; and (4) it can generate effective Web-page recommendations based on a set of thoughtfully-designed recommendation strategies. A prototype of the semantic-enhanced WPR system is developed and presented, and the experimental comparisons with existing WPR approaches convincingly prove the significantly improved performance of WPR systems based on the framework and its enabling techniques

Ontology-style web usage model for semantic Web applications

Current semantic recommender systems aim to exploit the website ontologies to produce valuable web recommendations. However, Web usage knowledge for recommendation is presented separately and differently from the domain ontology, this leads to the complexity of using inconsistent knowledge resources. This paper aims to solve this problem by proposing a novel ontology-style model of Web usage to represent the non-taxonomic visiting relationship among the visited pages. The output of this model is an ontology-style document which enables the discovered web usage knowledge to be sharable and machine-understandable in semantic Web applications, such as recommender systems. A case study is presented to show how this model is used in conjunction of the web usage mining and web recommendation. Two real-world datasets are used in the case study. © 2010 IEEE

Recommender Systems for the Semantic Web

This paper presents a semantic approach to Recommender Systems (RS), to exploit available contextual information about both the items to be recommended and the recommendation process, in an attempt to overcome some of the shortcomings of traditional RS implementations. An ontology is used as a backbone to the system in the proposed architecture to represent the problem domain, while multiple web services are orchestrated to compose a suitable recommendation model, matching the current recommendation context at run-time. In order to allow for such dynamic behaviour, the proposed system tackles the recommendation problem by applying existing RS techniques on three different levels: the selection of appropriate sets of features, recommendation model and recommendable items

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