HAS IT REALLY IMPROVED? KNOWLEDGE GRAPH BASED SEPARATION AND FU-SION FOR RECOMMENDATION

Abstract

In this paper we study the knowledge graph (KG) based recommendation systems. We first design the metric to study the relationship between different SOTA models and find that the current recommendation systems based on knowledge graph have poor ability to retain collaborative filtering signals, and higher-order connectivity would introduce noises. In addition, we explore the collaborative filtering recommendation method using GNN and design the experiment to show that the information learned between GNN models stacked with different layers is different, which provides the explanation for the unstable performance of GNN stacking different layers from a new perspective. According to the above findings, we first design the model-agnostic Cross-Layer Fusion Mechanism without any parameters to improve the performance of GNN. Experimental results on three datasets for collaborative filtering show that Cross-Layer Fusion Mechanism is effective for improving GNN performance. Then we design three independent signal extractors to mine the data at three different perspectives and train them separately. Finally, we use the signal fusion mechanism to fuse different signals. Experimental results on three datasets that introduce KG show that our KGSF achieves significant improvements over current SOTA KG based recommendation methods and the results are interpretable.



The recommendation system is the important technique for information filtering, which can help users find the data they want in a large amount of data. Collaborative filtering algorithm is a classical recommendation method, and its main idea is to make recommendations by mining the collaborative signals between users and items. As a deep learning method, graph neural networks (GNN) have been used to effectively mine users' collaborative signals, such as NGCF (Wang et al., 2019d) , LightGCN (He et al., 2020) . Recent works (Wu et al., 2021; Yu et al., 2022) use LightGCN as the backbone to introduce contrastive learning and achieve better performance. From LightGCN (He et al., 2020) to SimGCL (Yu et al., 2022) , the performance is constantly improving. For the convenience of description, we assume that there are two models, M1 and M2. The overall performance of M1 is better than that of M2. In practical applications, for some specific users, the recommendation effect of M1 may be inferior to that of M2. A problem that may be overlooked is, is M1 really an improvement over M2 ? That is, what is the relationship between M1 and M2? Figure 1 shows two possible relationships between M1 and M2. (i 1 ∼ i 6 are commodities, the circle "Test" represents the range of the test set, the circle "M1" represents the range of top K commodities given by the M1 model, the cricle "M2" represents the range of the test the top K commodities given by the M2 model). In Figure 1 (a), M1 learns new things on the basis of retaining the infor-



Figure 1: Two categories of improvement

