Comment-driven deep sequence recommendation method

Abstract

The invention discloses a comment-driven deep sequence recommendation method, which comprises the following steps of: establishing a vocabulary for a user comment text, and endowing each word with a randomly initialized word vector; constructing a document word vector expression matrix for each document; obtaining an aspect-perceived document expression tensor and a plurality of feature maps; calculating the long-term preference vector of the user and the vector representation of the commodity; calculating user short-term preference vectors of a joint level and an individual level; performingweighted addition on the two levels to obtain a final user short-term preference vector; multiplying the short-term preference vector of the user by a reduction coefficient, adding the short-term preference vector of the user to the long-term preference vector of the user to obtain vector representation of the user, and calculating a preference score of the user for the commodity; training and obtaining an RNS model; and applying the trained RNS model to an online sequence recommendation scene. The comment-driven sequence recommendation problem is well solved, the method has the advantages ofbeing high in training speed and short in test time, and it is shown that the method has wide practical significance and commercial value.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a review-driven deep sequence recommendation method. Background technique [0002] With the rapid rise of the Internet, we have gradually entered the era of big data. The information in the era of big data is complicated, and almost everyone faces overload of information every day. This is where the intelligent recommendation system comes into play. It has played a huge role in news browsing software, short video platforms, and question-and-answer communities. Recommendations help users alleviate the worries brought about by the information explosion. [0003] Traditional recommendation system technologies, such as the collaborative filtering framework represented by matrix decomposition, consider the user's interest preferences under a static thinking, that is, each user corresponds to a constant vector, but this is not in line with the actual situation. In reality, users' i...

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Problems solved by technology

[0004] In the current prior art, no one has proposed a serialized dy

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