Interpretability recommendation method based on knowledge graph path

Abstract

The invention discloses an interpretability recommendation method based on a knowledge graph path. The method comprises the steps of obtaining the interaction history of a user, taking the interactionhistory as a seed set of a knowledge graph, and obtaining a user-from the seed set; on the premise that a seed set is obtained, constructing a triad query corresponding to a knowledge graph on the seed set, extracting triads, generating a path representation through semantics of combined entities and relations in triad information, and conducting the reasoning process according to paths to deduceuser preferences; after the determination of a triple path, querying other paths from the head entity to the tail entity of the path on the premise of limiting the length of the path to be 4, and representing the paths by using a plurality of triples; after the finding of multiple paths, performing pool operation on each path to distinguish contributions of different paths to prediction recommendation; and selecting the path with the maximum contribution score to carry out interpretive recommendation on the user. The method is high in recommendation precision, and the problem of opaqueness ofrecommendation is solved.

Description

technical field [0001] The present invention relates to the technical field of recommendation algorithms, in particular to an interpretable recommendation method based on knowledge map paths. Background technique [0002] Nowadays, with the improvement of living standards, people will dig out more of their potential interests, and the existing technology also helps people find points of interest and recommend some potential activities or products to users. In recommendation technology, The introduction of auxiliary information knowledge graph into the recommendation system has attracted more and more attention. This method improves the accuracy of the recommendation, but the ensuing question is: for example, why do people buy the products recommended by the system or participate in the activities recommended by the system? ? This brings the problem of opacity to system recommendation. The key to solving the problem of opacity is to make the recommendation interpretable. Fo...

AI Technical Summary

Problems solved by technology

[0002] Nowadays, with the improvement of living standards, people will dig out more of their potential interests, and the existing technology also helps people find points of interest and recommend some potential activities or products to users. In recommendation technology, The introduction of auxiliary information knowledge graph into the recommendation system has attracted more and more attention. This method improves the accuracy of the recommendation, but the ensuing question is: for example, why do people buy the products recommended by the system or participate in the activities recommended by the system? ? This brings the problem of opacity to system recommendation

However, existing work has not sufficiently studied this connectivity to infer user preferences, especially in terms of modeling sequential dependencies within paths and overall semantics.

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