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Recommendation system cold start solving method based on user feedback

A recommendation system and solution technology, applied in the field of cold start of the recommendation system, to achieve the effect of improving the accuracy of the recommendation

Inactive Publication Date: 2017-01-11
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method aims at the problem of how to recommend new users in a personalized recommendation system, and provides a method that does not require additional information and quickly screens out products that users are interested in based on a limited number of user feedback interactions.

Method used

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Embodiment Construction

[0019] The basic idea of ​​the present invention is: for a new user, introduce the probability idea in the multi-armed bandit problem (multi-armed bandit problem) to divide each recommendation into two modes of exploration and development. According to the user's feedback results, the user's characteristics are adjusted, and the optimal recommendation strategy is obtained in a relatively small number of tests. Exploration refers to randomly recommending products to users, observing and recording user feedback, and increasing data samples; development refers to selecting products that users are most likely to buy based on current data samples for recommendation, increasing the success rate of recommendation. Dividing the recommendation into exploration and development is to quickly obtain the real needs of users in a short user interaction. Exploration is an essential part of improving the performance of personalized recommendation systems. The present invention will be furthe...

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Abstract

The invention provides a recommendation system cold start solving method based on user feedback. The method comprises the following steps: selecting data samples; constructing a time sequence sample matrix, dividing a user-commodity real score matrix into a plurality of sub matrixes according to a time sequence, simulating emergence of new users, taking sub matrixes in the top of time rank as training sub matrixes and taking other sub matrixes as test sub matrixes; and establishing a user-commodity characteristic matrix by using a latent semantic model, introducing new users into a confidence interval upper bounded UCB algorithm model and iterating and updating user characteristics and commodity characteristics. The recommendation system cold start solving method does not need extra information and is capable of rapidly screening commodities interested by the users according to limited frequency of user feedback interaction.

Description

technical field [0001] The invention relates to a personalized recommendation technology, in particular to a cold start method for a recommendation system. Background technique [0002] With the rapid development of the Internet and the rapid emergence of new content and new products, users spend more and more time choosing the information they want, resulting in a decrease in the efficiency of information use. For information overload, there are currently two solutions: search and recommendation. Search keywords to find the desired information, but the search provides the same results for all users, without taking into account the differences between different users. In order to better solve the problem of information overload, a personalized recommendation system came into being, which is an advanced business intelligence platform based on massive data mining, to help e-commerce websites provide fully personalized decision support for their customers. and information ser...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q30/02
CPCG06F16/337G06Q30/0201
Inventor 成石王宝亮毛陆虹常鹏
Owner TIANJIN UNIV
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