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

A neural network and recommendation method technology, applied in the field of data mining and recommendation system, can solve the problems of sparse user-POI check-in matrix and poor recommendation effect

Pending Publication Date: 2020-11-17
HANGZHOU DIANZI UNIV
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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-

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

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Embodiment Construction

[0025] The point of interest recommendation method based on the graph neural network and the user's long-term and short-term preferences provided by the present invention will be described in detail below, as figure 1 As shown, the present invention includes:

[0026] Step (1) Input the user's historical check-in data, where the check-in data includes the user's ID, POI ID, and check-in time.

[0027] Step (2) Read the sign-in data, and sort the access sequences of each user in chronological order. Create a user session sequence file, in which each row is a session sequence of points of interest for a user in a single day, in which the ID of the user is listed at the beginning of the line, and the IDs of the points of interest visited by the user on that day are listed in the user's list according to the order of access After the ID, the user ID and POI ID are separated by a space; the form is {user ID: POI ID}.

[0028] Step (3) Construct a directed graph of sessions: Model...

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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...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06F16/9537
CPCG06F16/9535G06F16/9537
Inventor 王东京张新俞东进王兴亮完颜文博
Owner HANGZHOU DIANZI UNIV
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