Collaborative filtering (CF) is a well-known and eminent recommendation technique to predict the preference of new users by revealing the structures of historical records of the examined users. Even though CF is effectively adapted in several commercial areas, many limitations still exist, particularly in the sparsity of rating data that raises many issues. This paper devises a novel deep learning strategy for CF to recognize user preferences. Here, black hole entropic fuzzy clustering (BHEFC) is devised for clustering item sequences to form groups with similar item sequences. Moreover, cluster centroids are optimized using the tunicate swarm magnetic optimization algorithm (TSMOA), which is devised by combining tunicate swarm algorithm and magnetic optimization algorithm. After grouping similar items together, the group matching is performed based on a deep convolutional neural network (Deep CNN). Subsequently, the visitor sequence and query sequence are compared using Jaro–Winkler distance, which contributes to the best visitor sequence. From this best visitor sequence, the recommended product is acquired. The proposed TSMOA–BHEFC and Deep CNN outperformed other methods with minimal mean absolute error of 0.200, mean absolute percentage error of 0.198 and root mean square error of 0.447, respectively.

中文翻译：

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用于协同过滤器推荐的深度卷积神经网络的 Tunicate Swarm 磁优化

协同过滤（CF）是一种著名的推荐技术，通过揭示被检查用户的历史记录结构来预测新用户的偏好。尽管 CF 在几个商业领域得到了有效的应用，但仍然存在许多限制，特别是在引发许多问题的评级数据的稀疏性方面。本文设计了一种新颖的 CF 深度学习策略来识别用户偏好。在这里，黑洞熵模糊聚类（BHEFC）被设计用于聚类项目序列以形成具有相似项目序列的组。此外，聚类质心使用被囊群磁优化算法（TSMOA）进行优化，该算法是由被囊群算法和磁优化算法相结合设计的。将相似的项目组合在一起后，组匹配是基于深度卷积神经网络（Deep CNN）进行的。随后，访问者序列和查询序列使用 Jaro-Winkler 距离进行比较，这有助于获得最佳访问者序列。从这个最佳访问者序列中，获得推荐的产品。所提出的 TSMOA-BHEFC 和 Deep CNN 分别以 0.200 的最小平均绝对误差、0.198 的平均绝对百分比误差和 0.447 的均方根误差优于其他方法。