Publications

Abstract : Serendipity is a critical factor in the Recommender Systems (RS) in delivering pleasantly surprising, novel, yet contextually relevant recommendations. Most existing methods improve serendipity in RS by learning user preferences based on item popularity or similarity. However, the effectiveness of these methods in mitigating popularity bias and generating novel and unexpected item recommendations remains poorly understood. Recent studies suggest improvement in user preference by incorporating textual opinion provided by the user on an item. Additionally, the trade-off relationship between serendipity's conflicting components, including relevance, novelty, and unexpectedness, warrants further investigation to improve the quality of top-n recommendations. Hence, this research proposes an opinion mining-based approach to learn the users' personalized preferences from the textual reviews and incorporate both rating and reviews preferences to improve the quality of the recommendation list. Next, we design a new Two-Fold Algorithmic (TFA) approach-based objective function for serendipity to mitigate the popularity bias by aggregating uncertainty based on item popularity and item similarity to user preferences. Lastly, a multi-objective evolutionary algorithm-based Serendipity Objective Optimization-based Recommendation Framework(SOORF) is designed to optimize the serendipity's conflicting components. Extensive simulations are conducted over four benchmark datasets. The Mean Absolute Precision(MAP)@n and Serendipity@n based evaluation findings of SOORF and TFA demonstrate an improvement of at least 8.10% and 58.48%, respectively. The Precision@n and Recall@n based evaluations on different dataset sparsity conditions are observed with an improvement of at least 13.59% and 27.73% over baseline models. The Pareto front shows the models' ability to generate surprising, novel, yet relevant recommendations.