# Predicting Dynamic User-Item Interaction with Meta-Path Guided Recursive RNN
## 1 Introduction  
In the era of big data, the large volume of online information generated in real time makes it very difficult for people to quickly find the valuable information they need. The recommendation algorithm is effective to solve the current information overload problem by recommending useful information to users while filtering out the less relevant information [2, 3]. For example, in some online multimedia websites, user experience canbe greatly improved by recommending the movies or songs to them which they may be interested in [4]. In many e-commerce platforms, recommending products that users would like to buy can help save their time and money [5]. Thus, accurately predicting the interactions between users and items is critically important to recommendation [6; 7; 8]. Figure 1a shows an example of the sequential interactions between two users ([PERSON] and [PERSON]) and the items. One can see that [PERSON] first buys a cell phone, and shortly he buys a phone case and a phone film. Thus, we can infer that he is more likely to buy a mobile earphone rather than a suit. Figure 1b shows an example of user-item interaction on an e-commerce platform. Each arrow represents an interaction from a user to an item, e.g., a user buying or browsing a commodity on e-commerce platform Taobao, with each interaction associated with a timestamp \(t\) and a feature vector \(f\) (such as the interaction types, users and commodity features). An interaction between a user and an item can be a user clicking, buying, or browsing a certain commodity on the online shopping website. Increasing research efforts have been devoted to the research topic of recommender system and great progress has been made recently [9; 10]. Inspired by the successful applications in natural language processing (NLP), [PERSON] proposed to use the GRU module to process the session-based behavior sequence of users for predicting the next item [11]. The interaction sequence is input into an LSTM module for adapting the dynamics of users and items. [PERSON] et al. [12] applied the graph neural network architecture to knowledge graph to acquire rich auxiliary information for the recommendation task. However, there are three major issues when applying such methods directly to our studied problem. First, as shown in Figure 1a, the successive online behaviors of users are usually highly correlated but are largely ignored by existing collaborative filtering-based recommender systems [13; 14]. Intuitively, if a user first buys a suit and then buys a pair of shoes, the next item that the user is more likely to buy in the near future is an apparel product rather than an electronic product such as a mobile phone. Second, some existing works only pay attention to exploring the interaction relationships between users and items, while ignoring the relationships between users and users as well as items and items. User-user and item-items interactions are important auxiliary information to solve the data sparsity issue. Third, the users' preference and the item popularity evolve over time [15; 16]. Existing works mostly learn a static represent vector for each user and item but fail to capture the dynamic representations of users and items that evolve over time. Figure 1: (**a**) Illustration of two user interaction sequences: [PERSON] buys a cell phone, a phone case, a phone film, and a mobile earphone; [PERSON] buys a suit, a dress, a shoe, and a hat, successively. (**b**) A toy example of an interaction network containing three users and four items. Each arrow represents an interaction from a user to an item. Each interaction is associated with a timestamp \(t\) and a feature vector \(f\) (such as the feature of the commodity). To address the above issues, in this paper we propose a Meta-path guided Recursive RNN based Shift embedding method named MRRNN-S to more effectively learn the dynamic representations of users and items, based on which the future user-item interactions can be more accurately predicted.