Weighted Bipartite Graph Model for Recommender System Using Entropy Based Similarity Measure

Collaborative filtering technique is widely adopted by researchers to generate quality recommendations. Constant efforts are being made by the researchers to generate quality recommendations thus satisfying and retaining the user. This work is an effort to generate quality recommendations by proposing a collaborative filtering approach. The proposed work models the sparse rating data as a weighted bipartite graph which represents data flexibly and exploits the graph properties to generate recommendations. In the proposed work user similarity is formulated as measure of entropy and cosine similarity which takes into account the relative difference between the ratings. Performance of the proposed approach is compared with the traditional collaborative filtering technique using Precision, Recall and F-Measure. Experiments were conducted on public and private datasets namely MovieLens and News dataset respectively. Results indicate that the performance of the proposed approach outperforms the traditional collaborative filtering approach.