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.

偶然性是推荐系统 (RS) 中提供令人惊喜、新颖但与上下文相关的推荐的关键因素。大多数现有方法通过基于项目流行度或相似性学习用户偏好来提高 RS 中的偶然性。然而，这些方法在减轻流行度偏差和生成新颖和意想不到的项目推荐方面的有效性仍然知之甚少。最近的研究表明，通过结合用户对项目提供的文本意见，可以改善用户偏好。此外，偶然性的冲突组成部分（包括相关性、新颖性和意外性）之间的权衡关系值得进一步调查，以提高 top-n 推荐的质量。因此，本研究提出了一种基于意见挖掘的方法来学习用户的 来自文本评论的个性化偏好，并结合评分和评论偏好以提高推荐列表的质量。接下来，我们设计了一种新的基于双重算法 (TFA) 方法的偶然性目标函数，通过聚合基于项目流行度和项目与用户偏好的相似性的不确定性来减轻流行度偏差。最后，设计了一种基于多目标进化算法的基于偶然性目标优化的推荐框架（SOORF）来优化偶然性的冲突组件。在四个基准数据集上进行了广泛的模拟。基于平均绝对精度 (MAP)@n 和 Serendipity@n 的 SOORF 和 TFA 评估结果分别显示了至少 8.10% 和 58.48% 的改进。在不同数据集稀疏条件下，基于 Precision@n 和 Recall@n 的评估与基线模型相比至少提高了 13.59% 和 27.73%。帕累托前沿显示了模型生成令人惊讶、新颖但相关的建议的能力。