Exploiting Semantic Web Technologies for Recommender Systems a Multi View Recommendation Engine
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
Abstract
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.
Introduction
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
Houda OUFAIDA, Omar NOUALI
DTISI Laboratory, CERIST Research Center 03, Rue frères Aissou - Ben Aknoun – Algiers, Algeria {houfaida, onouali}@mail.cerist.dz
Abstract
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.
Introduction
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