Exploiting Semantic Web Technologies for Recommender Systems a Multi View Recommendation Engine

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  • Topic: Recommender system, Semantic Web, Cold start
  • Pages : 13 (2542 words )
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  • Published : March 15, 2011
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Exploiting Semantic Web Technologies for Recommender Systems A Multi View Recommendation Engine Houda OUFAIDA, Omar NOUALI
DTISI Laboratory, CERIST Research Center 03, Rue frères Aissou - Ben Aknoun – Algiers, Algeria {houfaida, onouali}@mail.cerist.dz

Collaborative filtering systems are probably the most known recommendation techniques in the recommender systems field. They have been deployed in many commercial and academic applications. However, these systems still have some limitations such as cold start and sparsty problems. Recently, exploiting semantic web technologies such as social recommendations and semantic resources have been investigated. We propose a multi view recommendation engine integrating, in addition of the collaborative recommendations, social and semantic recommendations. Three different hybridization strategies to combine different types of recommendations are also proposed. Finally, an empirical study was conducted to verify our proposition.

Dealing with information overload is one of the most challenging problems in the information access field; the Web is a perfect example. Unlike retrieval systems (Google, AltaVista, Yahoo, ….) which succeed in selecting suitable items according to a specific user query, these items are the same for every user in every situation, recommender systems aim to make personalized recommendation to users according to their preferences, tastes and interests expressed by users themselves or learned by the recommender system over the time. There has been much work in this research area, from the early 1990 and still remains up to now. Foltz and Dumais experiences (Foltz and Dumais 1992) on four recommendation techniques have shown ambitious results, Resnick and collaborators proposed one of the first and probably the most known recommender system in the literature; Grouplens (Resnick et al. 1994) which recommends films to users according to their previous ratings. Since, several models were proposed in the literature and much more applications were developed in the industry. Examples of such applications include ecommerce websites like Amazon.com for recommending

books, CDs and different other items. MovieLens and Netflix for recommending movies and DVDs… Recently, a new generation called semantic and social recommender systems have emerged taking advantage of the advancements in the semantic web technologies and features such as ontologies, taxonomies, social networks, tagging. In this paper, we introduce a multi view recommender system that includes collaborative, social and semantic views of the user’s profile. Each view recommends a set of items. Hence, three hybridization strategies are proposed for recommendations re-ranking. Finally, results from our experimentations are presented. The rest of the paper is organized as follows: First we present the introduction of new Web 2.0 aspects in recommender systems. Then we expose our multi view recommender system, we present user’s multi view representation and then present three recommendation modules: collaborative, social and semantic matching, hybridization strategies are also exposed. Finally, we discuss our experimental results and conclude with a summary of conclusions and outlooks.

Related Work
The key for an efficient recommender system is better understanding of both users and items. However, traditional recommender systems consider limited data (ratings, keywords) to compute predictions and do not take into account different factors necessary to understand reasons behind a user’s judgment; is it the item’s content, quality, is it because a friend recommended it?… Consequently, the users’ classic communities’ reflects only a global similarity usually insufficient to describe relations connecting users and even more items. With the emergence of the Web 2.0, advancements allowed the apparition of a new generation of recommender systems: semantic and social recommender systems....
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