Hybrid bio-inspired user clustering for the generation of diversified recommendations

The research and development of recommender systems are traditionally focused on the enhancement and guaranteeing the recommendation accuracy to achieve user satisfaction. On the other hand, the alternative recommendation qualities such as diversity and novelty have received significant attention from researchers in recent times. In this paper, we present a detailed study of the diversity in recommender systems to help researchers in the development of recommendation approaches to generate efficient recommendations. We have also analyzed the existing works for assessment of impact and quality of diversified recommendations. Based on our detailed investigation of the diversity in recommendations, we shift the generic focus from accuracy objectives to explore beyond the accuracy of recommendations. The need for recommender systems producing diversified recommendations without compromising the accuracy is very high to meet the growing demands of users. To address the personalization problem in travel recommender systems, we present the hybrid swarm intelligence clustering ensemble-based recommendation framework to generate diverse and accurate Point of Interest recommendations. Our proposed recommendation approach employs multiple swarm optimization algorithms to frame a clustering ensemble for the generation of efficient user clustering. We have evaluated our proposed recommendation approach over a real-time large-scale dataset of TripAdvisor to estimate the quality of recommendations in terms of diversity and accuracy. The experimental results demonstrate the enhanced efficiency of the proposed recommendation approach over state-of-the-art techniques.