Interest point recommendation method based on graph neural network and user long-term and short-term preferences

Abstract

The invention provides an interest point recommendation method based on a graph neural network and user long-term and short-term preferences. The method comprises the following steps: taking a point-of-interest sequence accessed every day in historical sign-in data of a user as a session sequence; creating a directed graph based on the sessions, where each session sequence is regarded as a sub-graph, indicating that each node represents a point of interest, and each directed edge represents that a user accesses a pointed point of interest after accessing a source point of interest of each edge. Based on this graph, relationships between points of interest are captured by a graph neural network and vector representations of the points of interest are accurately generated. Based on the representation vectors of the interest points, the interest points to be accessed in the next step are recommended for the user by combining an attention mechanism. According to the invention, a better geographic information model is fused from the perspectives of users and the interest points. Therefore, the geographic distance between the users and the interest points and the sign-in frequency of theusers on the adjacent interest points are used in the model, and the problem of sign-in data sparseness is solved.

Description

technical field [0001] The invention belongs to the technical field of data mining and recommendation systems, and in particular relates to a point-of-interest recommendation method based on a graph neural network and long-term and short-term preferences of users. Background technique [0002] In recent years, with the rapid growth of location-based social networking services such as Foursquare, Gowalla, and Dianping, more and more users share their favorite points of interest through a large number of location check-ins. These online check-in data provide a good opportunity to analyze the user's check-in behavior pattern. We can analyze and predict where the user will go next based on the user's historical point of interest access trajectory. In addition, merchants can capture user preferences through mining and analysis of user check-in data, recommend content they are interested in for users, and improve user experience. Therefore, analyzing and recommending users' check...

AI Technical Summary

Problems solved by technology

Usually in these methods, due to the existence of a large number of POIs, each user can only access a few POIs, so the user-POI check-in matrix in collaborative filtering will become very sparse, resulting in poor recommendation effect. Difference

Images