The explosion of content on World Wide Web (WWW) means that consumers are presented with a wide variety of items to choose from (items that concur with their taste and requirements). The generation of personalized consumer recommendations has become a crucial functionality for many web applications, yet a challenging task, given the scale and nature of the data. One popular solution to creating personalized item suggestions to users is recommender systems. In this work, we propose an approach that integrates community detection with neighborhood-based recommender systems, specifically, the Adsorption algorithm, for recommending items using implicit user preferences. Network communities represent a principled way of organizing real-world networks into densely connected clusters of nodes. We believe that these dense clusters identified by the community detection algorithm will be helpful to construct user neighborhoods for Adsorption algorithm for recommending collaborators and books to users. Through comprehensive experimental evaluations on the DBLP co-author dataset and Book Crossing dataset, the proposed approach of integrating community detection with the Adsorption algorithm is shown to deliver good performance.
['Rohit Parimi', 'Doina Caragea']
Community Detection on Large Graph Datasets for Recommender Systems