Wasid Mohammed, Ali Rashid, Shahab Sana
Interdisciplinary Centre for Artificial Intelligence, Aligarh Muslim University, Aligarh, India.
Department of Computer Engineering, Aligarh Muslim University, Aligarh, India.
Heliyon. 2023 Jul 12;9(7):e18183. doi: 10.1016/j.heliyon.2023.e18183. eCollection 2023 Jul.
A Multi-Criteria Recommender System (MCRS) represents users' preferences on several factors of products and utilizes these preferences while making product recommendations. In recent studies, MCRS has demonstrated the potential of applying Multi-Criteria Decision Making methods to make effective recommendations in several application domains. However, eliciting actual user preferences is still a major challenge in MCRS since we have many criteria for each product. Therefore, this paper proposes a three-phase adaptive genetic algorithm-based approach to discover user preferences in MCRS. Initially, we build a model by assigning weights to multi-criteria features and then learn the preferences on each criteria during similarity computation among users through a genetic algorithm. This allows us to know the actual preference of the user on each criteria and find other like-minded users for decision making. Finally, products are recommended after making predictions. The comparative results demonstrate that the proposed genetic algorithm based approach outperforms both multi-criteria and single criteria based recommender systems on the Yahoo! Movies dataset based on various evaluation measures.
多标准推荐系统（MCRS）体现了用户对产品多个因素的偏好，并在进行产品推荐时利用这些偏好。在最近的研究中，MCRS已展示出在多个应用领域应用多标准决策方法以做出有效推荐的潜力。然而，在MCRS中引出实际用户偏好仍然是一个重大挑战，因为每个产品都有许多标准。因此，本文提出一种基于自适应遗传算法的三阶段方法来发现MCRS中的用户偏好。首先，我们通过为多标准特征分配权重来构建模型，然后在用户间的相似度计算过程中通过遗传算法学习每个标准上的偏好。这使我们能够了解用户在每个标准上的实际偏好，并找到其他志同道合的用户进行决策。最后，在进行预测后推荐产品。比较结果表明，基于遗传算法的方法在雅虎电影数据集上基于各种评估指标的表现优于基于多标准和单标准的推荐系统。
