Wu Buchen, Qin Jiwei
School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi 830046, China.
Entropy (Basel). 2022 May 31;24(6):778. doi: 10.3390/e24060778.
Although most list-ranking frameworks are based on multilayer perceptrons (MLP), they still face limitations within the method itself in the field of recommender systems in two respects: (1) MLP suffer from overfitting when dealing with sparse vectors. At the same time, the model itself tends to learn in-depth features of user-item interaction behavior but ignores some low-rank and shallow information present in the matrix. (2) Existing ranking methods cannot effectively deal with the problem of ranking between items with the same rating value and the problem of inconsistent independence in reality. We propose a list ranking framework based on linear and non-linear fusion for recommendation from implicit feedback, named RBLF. First, the model uses dense vectors to represent users and items through one-hot encoding and embedding. Second, to jointly learn shallow and deep user-item interaction, we use the interaction grabbing layer to capture the user-item interaction behavior through dense vectors of users and items. Finally, RBLF uses the Bayesian collaborative ranking to better fit the characteristics of implicit feedback. Eventually, the experiments show that the performance of RBLF obtains a significant improvement.
尽管大多数列表排序框架基于多层感知器（MLP），但在推荐系统领域，它们在方法本身仍存在两方面的局限性：（1）MLP在处理稀疏向量时存在过拟合问题。同时，模型本身倾向于学习用户-物品交互行为的深度特征，但忽略了矩阵中存在的一些低秩和浅层信息。（2）现有排序方法无法有效处理具有相同评分值的物品之间的排序问题以及现实中独立性不一致的问题。我们提出了一种基于线性和非线性融合的列表排序框架，用于从隐式反馈中进行推荐，名为RBLF。首先，该模型通过独热编码和嵌入使用密集向量来表示用户和物品。其次，为了联合学习浅层和深层的用户-物品交互，我们使用交互抓取层通过用户和物品的密集向量来捕获用户-物品交互行为。最后，RBLF使用贝叶斯协同排序来更好地拟合隐式反馈的特征。最终，实验表明RBLF的性能有显著提升。
