Recommendation system cold start method based on multi-arm bandit confidence upper limit

Abstract

The invention relates to a recommendation system cold start method based on multi-arm bandit confidence upper limit. The method comprises steps of: collecting data to build and preprocess a commoditydata set and obtaining a commodity dominant characteristic in a standard format; according to the commodity dominant characteristic, constructing a commodity recessive feature based on a latent Dirichlet algorithm, setting an output commodity recessive feature dimension, and remarking a commodity; creating a candidate commodity set based on the commodity data set: clustering the commodity data setaccording to the commodity recessive feature, clustering the commodities, wherein the commodities in the same cluster have similar properties and the commodities in different clusters have differentproperties, and extracting a commodity from each cluster at random to create a candidate commodity set; view the selection of an optimal commodity from the candidate commodity set as a multi-arm bandit problem, calculating the commodity with the highest score as a recommended product based on a confidence interval upper limit algorithm; after the commodity with the highest score in the candidate commodity set is recommended to a user, updating a user feature and a weight parameter according to the feedback.

Description

technical field [0001] The invention relates to a personalized recommendation technology, in particular to a cold start method of a recommendation system based on a multi-armed gambling machine confidence upper limit. technical background [0002] With the rapid development of information technology, the Internet generates massive amounts of data at an explosive rate every day, people's ability to produce, copy, and disseminate information is greatly enhanced, and every user becomes a producer of Internet information. Users spend more and more time selecting the information they need, and they can't even filter it independently, which leads to a decrease in the efficiency of information use, and the large amount of information becomes a burden instead, and the problem of information overload appears. In order to better solve the problem of information overload, a personalized recommendation system has emerged as the times require. This system can accurately predict user need...

AI Technical Summary

Problems solved by technology

Users spend more and more time selecting the information they need, and they can’t even filter it independently, which leads to a decrease in the efficiency of information use, and the large amount of information becomes a burden instead, and the problem of information overload appears.

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