Abstract
With the rapid increase in the popularity of big data and internet technology, sequential recommendation has become an important method to help people find items they are potentially interested in. Traditional recommendation methods use only recurrent neural networks (RNNs) to process sequential data. Although effective, the results may be unable to capture both the semantic-based preference and the complex transitions between items adequately. In this paper, we model separated session sequences into session graphs and capture complex transitions using graph neural networks (GNNs). We further link items in interaction sequences with existing external knowledge base (KB) entities and integrate the GNN-based recommender with key-value memory networks (KV-MNs) to incorporate KB knowledge. Specifically, we set a key matrix to many relation embeddings that learned from KB, corresponding to many entity attributes, and set up a set of value matrices storing the semantic-based preferences of different users for the corresponding attribute. By using a hybrid of a GNN and KV-MN, each session is represented as the combination of the current interest (i.e., sequential preference) and the global preference (i.e., semantic-based preference) of that session. Extensive experiments on three public real-world datasets show that our method performs better than baseline algorithms consistently.
Highlights
Recommender systems play an increasingly important role in helping users find items they are interested in by proactively recommending items in a variety of scenarios (e.g., e-commerce, news website and social networking sites)
(2) Recurrent neural networks (RNNs) have improved the performance of the recommender system effectively [4,5]. It is very limited in its ability to explicitly capture the complex transitions among distant items or the fine-grained user interest from the sequence of user behavior, but it should be taken into account for a sequential recommender system
Effective above, we argue that incorporating the external knowledge base into the sequential recommender system can improve the system capability to capture fine-grained preferences and incorporating complex transitions among distant items can improve the accuracy of the item embedding vectors, which are difficult to solve by most of the existing sequential recommendation methods
Summary
Recommender systems play an increasingly important role in helping users find items they are interested in by proactively recommending items in a variety of scenarios (e.g., e-commerce, news website and social networking sites). By linking the items in interaction sequences with the entities in the external knowledge graph, the RNN component captures a sequential representation for the user, while the KV-MN component captures the representation at the attribute level This approach has limited representation power in explicitly capturing the complex transitions [8,9]. We propose a knowledge-enhanced recommendation method using graph neural networks and memory networks to tackle the sequential recommendation task and overcome the limitations mentioned above This consists of a current interest component (i.e., sequential preference) and a global interest component (i.e., attribute-level preference). To model the current interests of users, we model separated session sequences into session graphs and capture complex transition information between items using graph neural networks
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