# Predicting Dynamic User-Item Interaction with Meta-Path Guided Recursive RNN
## 1 Introduction
Inspired by the previous work [1], we argue that the previously proposed RRNN-S model can still be further improved in terms of processing the original data. RRNN-S only utilized the second-order graph structural information but ignored the fact that the sequence data and the heterogeneity hidden in the graph can also provide auxiliary information. Therefore, we propose to add two new modules, namely the word2 vec module and the skip-gram based meta-path module to the RRNN-S model. The word2 vec module treats the sequential interaction data as sentences and then extracts the semantic information of the sentences by using the word2 vec method. The ship-gram-based meta-path module models the user-item interaction data as a heterogeneous graph to more effectively capture the heterogeneous information, and then learns the node embeddings on the graph. MRRNN-S first uses a word2 vec module to capture the features from the user-item interaction sequence. A skip-gram-based meta-path module is proposed to capture the heterogeneous information and higher-order proximity from the user-item interaction graph. Next, a recursive RNN module is also designed to catch the sequential dependence of user-item interactions simultaneously by mapping users and items into the same latent representation space. The embeddings of users and items are mutually and iteratively updated by the proposed recursive RNN. Then, a shift embedding module is designed to predict the continuous future embedding of a user through the time interval and then predict the user embedding. Finally, we predict the embedding of the item and identities the item whose embedding vector is closest to it in the embedding space. To summarize, our main contributions are as follows:  
* To acquire the rich auxiliary information from the user-item interaction data, we model the original user-item interaction data as sequences and graphs, respectively. A word2 vec module is proposed on the interaction sequence of each user, which aims to learn the initial embedding that preserves the sequential pattern and semantic information from sequences. Then a skip-gram-based meta-path module is proposed to the user-item interaction graph for capturing the heterogeneous information and the higher-order user-item relationships. * We propose to apply the GCN module to learn the node features of the user-item interaction graph, so that the similar users or items are closer in the feature space. * Comprehensive experiments are conducted over three user-item interaction graph datasets. The result demonstrates the effectiveness of our method against several competitive baselines. The proposed MRRNN-S is an extended version of the RRNN-S model which was proposed in our earlier paper published in ADMA2020. Next, we briefly describe the difference between this paper with our previous conference paper. We extract the static embedding with a newly designed module to better capture the auxiliary information. The previous work [1] only considered the structure information from the subgraphs extracted from the user-item interaction graph when exploring the static user and item embeddings. This work also improves the model's capability of feature extraction by transforming the original data into the interaction sequence of each user and the user-item interaction graph, which can more effectively learn the embeddings by preserving the sequential information as well as the heterogeneous graph information. We also re-conduct most of the experiments and add several new experiments to demonstrate the superiority of MRRNN-S. The remainder of this paper is organized as follows. We will first discuss related works in Section 2. Then Section 3 will give some notations and a formal definition of the studied problem. Section 4 will introduce the proposed model MRRNN-S and the objective function. In Section 5, we will evaluate our approach and report the results. Finally, the conclusion will be given in Section 6.