Causal reasoning method for correcting popularity deviation of recommendation system

A technology of recommendation system and reasoning method, which is applied in the field of causal reasoning to correct the deviation of recommendation system popularity. It can solve problems such as difficult tuning and highly sensitive weighting strategy, and achieve the effect of reducing the time and difficulty of adjusting parameters and improving recommendation performance.

Active Publication Date: 2021-07-23
UNIV OF SCI & TECH OF CHINA
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While this method is theoretically sound, its weighting

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  • Causal reasoning method for correcting popularity deviation of recommendation system
  • Causal reasoning method for correcting popularity deviation of recommendation system
  • Causal reasoning method for correcting popularity deviation of recommendation system

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[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0017] The embodiment of the present invention discusses the problem of popularity deviation from a novel and basic perspective—causal relationship, and proposes a causal reasoning method to correct the popularity deviation of the recommendation system, which is a model-independent counterfactual reasoning Method framework (MACR). It is found that popularity bias exists in the direct influence from item nodes to ranking scores, so intrinsic propert...

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Abstract

The invention discloses a causal reasoning method for correcting popularity deviation of a recommendation system. The causal reasoning method comprises the following steps: acquiring a matching score of a user and an article in the current recommendation system; predicting an article score according to the popularity degree of the article, and predicting a user score according to the preference of the user; and aggregating the matching score of the user and the article, the article score and the user score, predicting the matching score of the user and the article, and removing the influence caused by popularity deviation to obtain the final matching score of the user and the article. The method provided by the invention is a model-independent anti-fact reasoning framework, can be suitable for various recommendation systems, and can provide high-quality and accurate personalized recommendation contents for users by eliminating popularity deviation and improving the recommendation performance of the recommendation systems.

Description

technical field [0001] The invention relates to the technical field of personalized recommendation, in particular to a causal reasoning method for correcting deviations in popularity of recommendation systems. Background technique [0002] Personalized recommendations have revolutionized many online applications, such as e-commerce, search engines, and conversational systems. A large number of recommendation models have been developed, where the default optimization choice is to reconstruct the historical user-item interactions. However, the frequency distribution of items in interaction data is never balanced, and it is affected by many factors such as exposure mechanism, word-of-mouth effect, sales activities, product quality, etc. In most cases, the frequency distribution of items is long-tailed, i.e., a small number of popular items have the vast majority of interactions in the dataset. This makes the classic training paradigm biased toward recommending popular items, ...

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Application Information

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IPC IPC(8): G06F16/9535G06N3/04G06N3/08G06N5/04
CPCG06F16/9535G06N5/04G06N3/08G06N3/045
Inventor 何向南魏天心冯福利陈佳伟易津锋
Owner UNIV OF SCI & TECH OF CHINA
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