Abstract
Using pre-trained topic information to assist in training neural networks can effectively support the completion of the rating prediction task. However, existing neural-topic methods consider only the use of topic information corresponding to current users and items without neighbors, whereas existing memory-based neighborhood approaches are inappropriate for the direct modeling of neighbors with topics. To address the limitations, we argue that memory networks have the ability to organize neighbors with corresponding topics well and can provide a general solution to this problem. To confirm our hypothesis, we propose two approaches. One is an augmented memory network to couple with and enhance existing neural-topic models. The other is a symmetric memory network activated by a memory reorganization mechanism, which is a compact and generalized method for rating prediction. The experimental results demonstrate the effectiveness of the memory reorganization mechanism and show that the two proposed methods have advantages over existing state-of-the-art topic modeling approaches.
Highlights
The fusion of topic modeling and matrix factorization (MF), called Collaborative Topic Modeling (CTM) [1], is an important strategy in recommendation research
The Adaptive Aspect Attention-based Neural Collaborative Filtering (A3NCF) [8] model seamlessly fuses topic vectors extracted from user and item reviews into a neural network and utilizes topic vectors from multiple levels
We have introduced the theory of the ReOrganizing Memory (ROM) framework, and its detailed implementation is shown in Algorithm 1, which is implemented by Keras
Summary
The fusion of topic modeling and matrix factorization (MF), called Collaborative Topic Modeling (CTM) [1], is an important strategy in recommendation research. Modeling directly from the perspective of NTCF will increase the complexity of the model, and existing methods rarely consider capturing the relationships uniformly to reduce the model complexity According to these limitations, we argue that: By establishing the correspondence between topics and neighbors, memory networks have the ability to learn neighbor features with the help of the fixed topic information. The Adaptive Aspect Attention-based Neural Collaborative Filtering (A3NCF) [8] model seamlessly fuses topic vectors extracted from user and item reviews into a neural network and utilizes topic vectors from multiple levels. This method does not consider how to make full use of neighbors’ topic vectors to better express user preferences. We propose a memory-enhanced approach and a unified memory modeling framework and compare them with each other
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