Multi-modal adversarial learning type video recommendation method and system

Abstract

The invention relates to the field of computers and artificial intelligence, and provides a multi-modal adversarial learning type video recommendation method and system. According to the method, imageinformation of recommended projects is introduced, key technologies such as hierarchical kernel descriptor features, cross-modal semantics and adversarial learning are fused into a Bayesian personalized sorting model, an MVABRP model is constructed, and a group of most relevant projects are optimized based on the MVABPR model and recommended to users. According to the method or system, the recommendation task can be completed based on the heterogeneous data (the user scoring matrix and the image), the problem of data sparsity in recommendation is relieved to a certain extent, and the individuation degree of recommendation is improved.

Description

technical field [0001] The present invention relates to the fields of computers and artificial intelligence, and more specifically, to a video recommendation method and system. Background technique [0002] In recent years, with the rapid development of Internet technology, Internet application products emerge in an endless stream. They have the advantages of strong interactive ability, convenient operation, and easy dissemination, and can carry rich network information. However, this has also promoted the explosive growth of the amount of information on the network, causing Internet users to be submerged in massive data and unable to extricate themselves, resulting in the problem of "information overload" (also known as "information explosion"). The recommendation system is one of the effective means to deal with the problem of "information overload". [0003] With the advent of the era of artificial intelligence (AI), recommendation systems integrating advanced technologi...

AI Technical Summary

Problems solved by technology

[0007] (1) The degree of personalization of the recommendation is not high, and the recommendation results that are diversified and close to their real preferences are not given according to the actual needs of users;

[0008] (2) Since the number of items browsed or rated by users is very limited, there is a serious "data sparse" problem in the user rating matrix, which seriously affects the recommendation performance;

[0009] (3) Only rely on matrix decomposition to obtain item feature representation, but lack of deep semantic description of recommended items;

[0010] (4) Mainly consider the explicit interaction between the user and the item, while ignoring those key implicit interactions, lacking the analysis of the potential interest of the user;

[0011] (5) The recommendation results are more inclined to long-term users, and the actual recommendation effect for new users is not good, that is, there is a "cold start" problem in the recommendation, and it is impossible to make relevant recommendations to new users

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