Anish Agarwal

Enhancing memory-based collaborative filtering techniques for group recommender systems by resolving the data sparsity problem. Comparing the proposed method's accuracy with basic memory-based techniques and latent factor model. Making accurate predictions for unknown ratings in sparse matrices based on the proposed method. More users are satisfied of the group recommender system's performance. Memory-based collaborating filtering techniques are widely used in recommender systems. They are based on full initial ratings in a user-item matrix. However, most of the time in group recommender systems, this matrix is sparse and users' preferences are unknown. Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, a content-metadata-based approach is suitable that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, the approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance.