Recommendation method and system, computer device and computer readable storage medium

Abstract

The invention relates to a recommendation method and system, and the method comprises the following steps: building a social network of a target user corresponding to an article set consumed by the target user; Establishing a dynamic personal interest model of the target user according to the article set; Constructing a short-term interest model of the social network according to the article set;Constructing a long-term interest model of the social network; Splicing is carried out according to the short-term interest model and the long-term interest model; Calculating the node representationof the target user and the node representation of friends in the social network; Calculating a combined feature weight according to the weight of friends about the target user in the social network; Performing nonlinear transformation on the combined feature weights; Calculating according to the dynamic personal interest model; Obtaining the probability of recommending articles according to the final interest of the user; Calculating a log-likelihood function value according to the probability of the recommended article; According to the technical scheme, the social relation of the user and the dynamic interest and hobby factors of the user can be considered at the same time, so that the recommendation accuracy is improved.

Description

technical field [0001] The present invention relates to the field of information recommendation, in particular to a recommendation method, a recommendation system, a computer device and a computer-readable storage medium. Background technique [0002] Hidasi et al. proposed a technique using LSTM for session-based recommendation. This method mainly uses the LSTM mentioned above to model the representation of each consumer item based on the user's historical consumption records. In order to recommend the next item, they use the last item consumed by the user to represent the user's current interest. Based on the mathematical representation of this interest, we calculate the similarity between the current interest and all items, and finally we recommend to the user the one that is most similar to the current interest. items. This technology is a mature technology, but it has many problems. First of all, it does not model the user's long-term interest, but only uses the user...

AI Technical Summary

Problems solved by technology

They modeled a trust factor to describe the influence of friends on a user's recommendation, and considered the influence of friends when modeling user interests, but this model has been proposed for a long time, and does not consider the serialization of consumption history features, so there are certain defects

[0003] Existing technologies either model the dynamic interests of users or analyze social influence in recommender systems, but as far as we know, there is no technology that combines the above two factors

A recent study on modeling session-level user behavior using recurrent neural networks without considering social influence

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