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A project-level and feature-level deep collaborative filtering recommendation algorithm based on an attention mechanism

A collaborative filtering recommendation and attention technology, applied in the information field, can solve problems such as rarely in-depth exploration of the implicitness of user preferences

Pending Publication Date: 2019-04-23
LIAONING TECHNICAL UNIVERSITY
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AI Technical Summary

Problems solved by technology

However, there are few methods to deeply explore the implicitness of user preferences

Method used

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  • A project-level and feature-level deep collaborative filtering recommendation algorithm based on an attention mechanism
  • A project-level and feature-level deep collaborative filtering recommendation algorithm based on an attention mechanism
  • A project-level and feature-level deep collaborative filtering recommendation algorithm based on an attention mechanism

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Embodiment Construction

[0053] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0054] Early item-based CF methods used cosine similarity, Pearson correlation, and modified cosine similarity to calculate item similarity. The basic idea is to predict the rating of user u on target item i Depending on the similarity of item i to all historically rated items of user u, the prediction model is as follows:

[0055]

[0056] where r uj Indicates user u’s rating on history item j, s ij Indicates the similarity between target item i and historical item j, R (u) is the user's historical interaction itemset. However, these traditional methods for computing item similarity lack personalization.

[0057] Kabbur proposed an item factor similarity model (Factored Item Similarity Model, FISM), according to the user's historical interaction item set R (u) To simulate the user's current preference, it regards the item a...

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Abstract

The invention discloses a project-level and feature-level deep collaborative filtering recommendation algorithm based on an attention mechanism. The algorithm comprises the following steps of S1, counting historical project scores of a user; S2, calculating the feature level content representation of the user on the target project according to the historical project score of the user; and S3, calculating a project-level prediction score of the user for the target project according to the historical project score of the user and the technical result of the S2. According to the algorithm, the recommendation precision is improved to a certain extent by combining attention mechanisms on a project level and a feature level, and compared with the prior art, the algorithm has higher interpretability in analysis of historical preferences of users. The extended DACFs, such as recently proposed neural collaborative filtering and discrete collaborative filtering, will also be considered in othercollaborative filtering models, a higher-order characteristic level attention mechanism is explored for future research, and the theoretical basis of research of the recommendation system is further tamped.

Description

technical field [0001] The invention relates to the field of information technology, in particular to an item-level and feature-level deep collaborative filtering recommendation algorithm based on an attention mechanism. Background technique [0002] With the advent of the Internet age, the amount of information has grown explosively, which has brought about the problem of "information overload". Personalized recommendation is one of the most effective ways to solve the problem of information overload. Unlike general search engines, the recommendation system learns User preferences and the relationship between users and items make recommendations for items that users may be interested in (e.g., movies, products, travel, and leisure activities), and have been adopted by Amazon, Google, Netflix, and others. Analyzing user feedback information and item description information, and mining the hidden features of the two are important components for users to make personalized reco...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06F16/9535
Inventor 王永贵尚庚
Owner LIAONING TECHNICAL UNIVERSITY
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