| Abstract | One of the major problems in the domain of social networks is the handling and diffusion of the vast, dynamic and disparate information created by its users. In this context, the information contributed by users can be exploited to generate recommendations for other users. Relevant recommender systems take into account static data from users' profiles, such as location, age or gender, complemented with dynamic aspects stemming from the user behavior and/or social network state such as user preferences, items' general acceptance and influence from social friends. In this paper, we enhance recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended. The inclusion of these aspects leads to more accurate recommendations and diffusion of better user-targeted information. This allows for better exploitation of the limited recommendation space, and therefore online advertisement efficiency is raised. |
