Dialogue recommendation method based on graph representation

Abstract

The invention discloses a session recommendation method based on graph representation. According to the method, based on historical interaction data of a given target user, the next item most likely to be interacted by the target user is found out. The method comprises the following steps: firstly, constructing a directed graph neural network of an article based on a user historical session sequence; and capturing a transfer relationship between the articles through a graph neural network; then modeling the current session of the user by using a long-short memory network to extract short-terminterests, and meanwhile, obtaining the long-term interests of the user from the recent session sequence of the user by using a maximum pool method; and finally, combining the short-term interests andthe long-term interests of the users to recommend the articles. The method disclosed by the invention overcomes the defects in the existing method: (1) the complex transfer relationship of articles in the session sequence cannot be captured; and (2) the long-term interest of the user is not considered to change along with time. Therefore, compared with an existing method, the recommendation effect implemented by the method is obviously improved.

Description

technical field [0001] The invention belongs to the technical field of Internet services, and in particular relates to a graph representation-based conversational social recommendation method. Background technique [0002] With the development and popularity of online services, online platforms record a large amount of user behavior data. Find out the items that users are most interested in from massive data and recommend them to users, which can greatly improve user satisfaction and company revenue. At this time, the recommendation system is very important. The recommendation system can dig out the user's favorite items from a large number of items. [0003] Traditional methods, such as content-based recommendation methods and collaborative filtering methods, only capture the static interaction information of users. In fact, user attributes and interactive activities are constantly updated, and this sequence data reflects the variability of user interests. Therefore, th...

AI Technical Summary

Problems solved by technology

However, these methods have some problems

First, it does not take into account that users' long-term interests will also change over time

Second, the transfer relationship between distant items and the spatial structure of the item network are ignored

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