Session recommendation model fusing user microcosmic behaviors and knowledge graph

Abstract

The invention belongs to the technical field of artificial intelligence and information retrieval, and particularly relates to a session recommendation model integrating user microcosmic behaviors anda knowledge graph, called MKMSR for short. According to the model, firstly, different deep learning models are used for encoding an interactive operation sequence and an interactive article sequenceof a user respectively, and embedded representation vectors of operations and articles are combined into microscopic behavior representation (vectors) of the user; a comprehensive representation (vector) of the session is generated based on the microscopic behavior representation of the user by adopting an attention mechanism, thereby realizing accurate prediction of the next interactive article.Besides, the model adjusts and optimizes item representation based on a TransH knowledge representation model, and integrates the item representation and the session recommendation task into a multi-task learning framework together, so that the session recommendation effect is further improved, and the problem caused by sparse historical interaction data of the user is effectively relieved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and information retrieval, and in particular relates to a conversation recommendation model based on user microscopic behavior and knowledge graph. Background technique [0002] In the era of big data, users can freely choose from a large number of goods and services, but there is an inevitable problem of information overload. The recommendation model emerged as the times require. The personalized recommendation model recommends information and products that users may be interested in based on the user's interests and behavior characteristics, helping users save time, tap the potential interests of users, and improve the interaction rate of products. Satisfaction and loyalty to the platform brings huge economic benefits to the platform, so it has become a basic tool that can help users make reasonable decisions and choices. [0003] Session recommendation (session-based recommenda...

AI Technical Summary

Problems solved by technology

[0004] Problem 1: Ignoring the micro-behavior in the interaction between users and items

[0008] Problem 2: Insufficient utilization of item knowledge

[0009] Most conversational recommendation models only infer item preferences based on items that have been interacted with in the past, so when the interaction records are sparse, the recommendation effect is poor

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