Dr. C. Selvi

The Recommender System (RS) plays an important role in information retrieval techniques in a bid to handle massive online data effectively. It gives suggestions on items/services to the target online user to ensure correct decisions quickly and easily. Collaborative Filtering (CF) is a key approach in RS providing a recommendation to the target online user, based on a rating similarity among users. Unsupervised clustering approach is a model-based CF, which is preferred as it ensures simple and effective recommendation. Such CFs suffer from a high error rate and needs additional iterations for convergence. This paper proposes a Modified k-means clustering approach to eliminate the above mentioned issues to provide well-framed clusters. The novel supervised Adaptive Genetic Neural Network (AGNN) method is proposed to locate the most favored data points in a cluster to deliver effective recommendations. The performance of the proposed RS is measured by conducting an experimental analysis on benchmark MovieLens and Netflix datasets. Results are compared with state-of-the-art methods namely Artificial Neural Network (ANN) and Fuzzy based RS models to show the effectiveness of the proposed AGNN method.

The enormous growth of Internet facilities, the user finds difficulties in choosing the music based on their current mindset. The context-aware recommendation has turned out to be well-established technique, which recommends a music based on the mindset of the user in various contexts. To enhance the potential of music recommendation, the emotion and time interval are considered as the most important context. Emotion context is not explored due to the difficulty in acquisition of emotions from user's microblogs on the particular music. This paper proposes an algorithm to extract the emotions of a user from microblog during a different time interval and represented at different granularity levels. Each music piece crawled from online YouTube repository is represented in a triplet format: (User{\_}id, Emotion{\_}vector, Music{\_}id). These triplet associations are considered for developing several emotion-aware techniques to provide music recommendations. Several trial of experimentation demonstrates that the proposed method with user emotional context enhances the recommendation performance in terms of hit rate, precision, recall, and F1-measure.