| Abstract | In contemporary internet architectures, including server farms and blog aggregators, web log data may be scattered among multiple cooperating peers. In order to perform content personalization through provision of recommendations on such architectures, it is necessary to employ a recommendation algorithm; however the majority of such algorithms are centralized, necessitating excessive data transfers and exhibiting performance issues when the number of users or the volume of data increase. In this paper we propose an approach where the clickstream information is distributed to a number of peers, which cooperate for discovering frequent patterns and for generating recommendations, introducing (a) architectures that allow the distribution of both the content and the clickstream database to the participating peers and (b) algorithms that allow collaborative decisions on the recommendations to the users, in the presence of scattered log information. The proposed approach may be employed in various domains, including digital libraries, social data, server farms and content distribution networks. |
