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What is Serendipity in Recommender Systems about?

This paper presents a novel approach for generating serendipitous recommendations using a Convolutional Neural Network (CNN) integrated with Particle Swarm Optimization (PSO). The method focuses on predicting focus shift points based on unexpectedness and relevance, aiming to provide users with novel and relevant recommendations that enhance satisfaction. Evaluation results indicate that the proposed approach outperforms existing methods in key performance metrics.

Author

Debarati Bhaumik

Language

EN