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Abstract
Due the success of emerging Web 2.0, and different social network Web sites such as Amazon and movie lens, recommender systems are creating unprecedented opportunities to help people browsing the web when looking for relevant information, and making choices. Generally, these recommender systems are classified in three categories: content based, collaborative filtering, and hybrid based recommendation systems. Usually, these systems employ standard recommendation methods such as artificial neural networks, nearest neighbor, or Bayesian networks. However, these approaches are limited compared to methods based on web applications, such as social networks or semantic web. In this paper, we propose a novel approach for recommendation systems called semantic social recommendation systems that enhance the analysis of social networks exploiting the power of semantic social network analysis. Experiments on real-world data from Amazon examine the quality of our recommendation method as well as the performance of our recommendation algorithms.
Keywords: Recommender system, social network, semantic web, user profile.
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1. Introduction
The prevalent use of computers and Internet has enhanced the quality of life for many people, tasks that were once done mostly through physical/human interactions, such as banking, shopping, or communication can now be done online; a seemingly simpler and better alternative. Also, with rapidly growing amount of information in the web, it is difficult to find needed information quickly and efficiently. That is where the recommender systems come in as a special type of information filtering. Nowadays many applications have used recommender systems; especially in the e-commerce domains such as http://www.amazon.com (see an example in Figure 1) where a failure recommendation could cause great losses of time, effort, and money. Our objective is to review a solution to surpass the defects of failure recommendation, by presenting semantic social recommendation approaches. The idea here is to combine two important aspects; the social aspect by using social network analysis measures, and the semantic aspect by using the semantic similarity measures.
The paper is organized as follows: section 2 presents related work, section 3 details the new recommendation model proposed, section 4 explains our obtained results, and section 5 concludes and discusses future work.
2. Background knowledge and related works
The approach described in this paper relies on a combination of social network...