On the Value of Random Opinions in Decentralized Recommendation

Abstract. As the amount of information available to users continues to grow, filtering wanted items from unwanted ones becomes a dominant task. To this end, various collaborative-filtering techniques have been developed in which the ratings of items by other users form the basis for recommending items that could be of interest for a specific person. These techniques are based on the assumption that having ratings from similar users improves the quality of recommendation. For decentralized systems, such as peer-to-peer networks, it is generally impossible to get ratings from all users. For this reason, research has focused on finding the best set of peers for recommending items for a specific person. In this paper, we analyze to what extent the selection of such a set influences the quality of recommendation. Our findings are based on an extensive experimental evaluation of the MovieLens data set applied to recommending movies. We find that, in general, a random selection of peers gives...

• Type: Conference
• Year: 2006
• Where: DAIS
• Authors: Elth Ogston, Arno Bakker, Maarten van Steen
• Keywords: DAIS 2006, DAIS 2007, Decentralized Recommendation Techniques, Specific Person, Various Collaborative-filtering Techniques