Abstract
Academic text recommendation, as a kind of text recommendation, has a wide range of application prospects. Predicting texts of interest to scholars in different fields based on anonymous sessions is a challenging problem. However, the existing session-based method only considers the sequential information, and pays more attention to capture the session purpose. The relationship between adjacent items in the session is not noticed. Specifically in the field of session-based text recommendation, the most important semantic relationship of text is not fully utilized. Based on the graph neural network and attention mechanism, this paper proposes a session-based text recommendation model (TXT-SR) incorporating the semantic relations, which is applied to the academic field. TXT-SR makes full use of the tightness of semantic connections between adjacent texts. We have conducted experiments on two real-life academic datasets from CiteULike. Experimental results show that TXT-SR has better effectiveness than existing session-based recommendation methods.
Highlights
We have the following observations from the results: (1) From the overall effect point of view, it can be observed that the performance of these three methods that use textual semantics (TXT-SR-C, TXT-SR-P, and TXT-SR-J) is better than that of TXT-SR-N, which can highlight the importance of textual semantics in the field of text recommendation
As the graph neural network needs to use the information of the constructed graph in the continuous training and iteration process, such information is the in-and-out matrix of the graph
This paper mainly proposes how to integrate the textual semantic relationship into the session-based recommendation system
Summary
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; Abstract: Academic text recommendation, as a kind of text recommendation, has a wide range of application prospects. Predicting texts of interest to scholars in different fields based on anonymous sessions is a challenging problem. However, the existing session-based method only considers the sequential information, and pays more attention to capture the session purpose. The relationship between adjacent items in the session is not noticed. Specifically in the field of session-based text recommendation, the most important semantic relationship of text is not fully utilized. Based on the graph neural network and attention mechanism, this paper proposes a session-based text recommendation model (TXT-SR) incorporating the semantic relations, which is applied to the academic field. TXT-SR makes full use of the tightness of semantic connections between adjacent texts. We have conducted experiments on two real-life academic datasets from CiteULike. Experimental results show that TXT-SR has better effectiveness than existing session-based recommendation methods.
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