A List-Ranking Framework Based on Linear and Non-Linear Fusion for Recommendation from Implicit Feedback.
A List-Ranking Framework Based on Linear and Non-Linear Fusion for Recommendation from Implicit Feedback.
复制标题
基于线性和非线性融合的隐式反馈推荐列表排序框架
DOI：
10.3390/e24060778
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发表时间：
2022-05-31
期刊：
影响因子：
2.7
通讯作者：
Qin, Jiwei
中科院分区：
文献类型：
--
作者：
Wu, Buchen;Qin, Jiwei
虽然大多数列表排序框架都是基于多层感知器（MLP）的，但它们在推荐系统领域仍然面临着两个方面的方法本身的局限性：（1）MLP在处理稀疏向量时会出现过拟合。同时，模型本身倾向于学习用户-项目交互行为的深层特征，而忽略了矩阵中存在的一些低秩和浅层信息。(2)现有的排序方法不能有效地处理现实中具有相同评分值的项目之间的排序问题和独立性不一致的问题。提出了一种基于线性和非线性融合的列表排序框架RBLF，用于隐式反馈推荐。首先，该模型使用密集向量来表示用户和项目，通过one-hot编码和嵌入。其次，为了共同学习浅层和深层用户-项目交互，我们使用交互抓取层通过用户和项目的密集向量来捕获用户-项目交互行为。最后，RBLF使用贝叶斯协作排序，以更好地适应隐式反馈的特点。实验结果表明，RBLF算法的性能得到了明显的改善。
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.
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