Recommendation system recall method based on attention mechanism

Abstract

The invention discloses a recommendation system recall method based on an attention mechanism, and the method comprises the following steps: extracting user features and commodity features in a training sample, converting the user features into a user embedding vector, and converting the commodity features into a commodity embedding vector; inputting the user embedding vector and the commodity embedding vector into an attention mechanism model for training, learning the weight of each feature through an attention network in the model, and performing weighted summation on the embedding vectorsof all the features according to the weights to obtain a user representation vector and a commodity representation vector; calculating an inner product of the user representation vector and the commodity representation vector to obtain a user commodity purchase intention matching degree of the training sample, establishing a cross entropy loss function of the user commodity purchase intention matching degree, calculating a minimized cross entropy loss function, and converging an attention mechanism model; and inputting the to-be-tested sample into the converged attention mechanism model, obtaining a user commodity purchase intention matching degree of the to-be-tested sample, and selecting a commodity of which the user commodity purchase intention matching degree is in a preset interval asa recall result for recommendation. According to the invention, the generalization is enhanced, and the calculation amount of recall recommendation is greatly simplified.

Description

technical field [0001] The invention relates to the field of computer recommendation systems, in particular to a method for recalling a recommendation system based on an attention mechanism. Background technique [0002] With the improvement of living standards, we have more and more choices for consumption. From shopping around to now, it takes a lot of time to find the goods we need, and even if we find what we want , may not be the most suitable for us. The recommendation system can help us find the relevant products we search for from the product pile, and recommend the products that are most suitable for us to us. Recommender systems are widely used today and are ubiquitous in life. When shopping online, we hope that the platform will recommend the products we just want to buy. When listening to music, we hope to hear songs that suit our taste. When searching for things, we hope that the search results will be what we are looking for. Therefore, fast and accurate pre...

AI Technical Summary

Problems solved by technology

However, there are few studies on applying the attention mechanism to the recommendation system model.

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