Meta Path-Aware Rating Collaborative Filtering in Heterogeneous Information Network

As an indispensable part of online shopping and e-commerce, recommendation system(RS) plays a vital role in helping users find products they are interested in. In many recommendation tasks, rating prediction is one of the most common and important ones. Moreover, among many recommendation models for rating prediction, Neighborhood-Based Collaborative Filtering(NBCF) has received a lot of attention and is widely used because of its advantages such as simplicity and interpretability. The main idea of the NBCF model is that similar users have similar preferences, which is relatively intuitive. However, NBCF is not able to obtain good recommendation performance only with historical user-item interaction information. The main reason is that the historical interaction information is often very sparse and the additional information is not enough, which makes the similarity measurement between users (items) less accurate. With the rapid development of networks, the information network contains many different types of objects and relationships, and more rich semantic information can be further tapped and utilized, which naturally constitutes Heterogeneous Information Network (HIN). So, HIN-based recommendation model has received extensive attention from researchers. Compared with traditional recommendation model, HIN-based recommendation models can not only effectively improve the recommendation performance, but also alleviate the problems of cold-start and data sparsity. However, most existing HIN-based recommendation models have many parameters which need to be learned while ensuring the validity, and the parameter setting has an important impact on the model's performance. Particularity, some of the parameters in these models need to be adjusted manually, which results in a large amount of training time. Therefore, this paper proposes a parameterless meta path-aware rating collaborative filtering recommendation model in HIN(HRCF). Moreover, HRCF can be seen as an extension of NBCF in HIN, which makes HRCF inherit many advantages of NBCF. But the main difference between them is that NBCF utilizes similarity between users (items) to make recommendations, while HRCF directly measures similarity between ratings in HIN to make rating predictions. The main idea of HRCF is that ratings of similar users on similar items are close, and our statistical results in the Yelp dataset confirm this idea. In general, HRCF is to find the rating's neighborhood through the meta-path in HIN and then weight the neighbors to estimate the rating. In addition, HRCF does not need to adjust or learn any parameters, and it can be converted to more convenient and fast matrix operations to perform. Specifically, we first generate commuting matrices between users (items) on different meta-paths. Then they are integrated to calculate the similarity matrix between users (items). Finally, the similarity matrix between users, the historical rating matrix of users-items, and the similarity matrix between items are sequentially multiplied and normalized to estimate all ratings at once. In order to verify the validity of the HRCF model, this paper conducted experiments on published Douban Book and Yelp datasets. The experimental results show that the recommendation accuracy of the HRCF model is better than the existing methods, and can well overcome the cold-start problem. Furthermore, we have also shown that a given set of meta-paths on those two datasets has a positive effect on the performance of the HRCF model. © 2020, Science Press. All right reserved.