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A recommendation method based on active learning to solve commodity cold start problem

A recommendation method and active learning technology, applied in the field of recommendation system, can solve problems such as errors and wrong recommendations

Active Publication Date: 2019-12-17
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The content-based recommendation algorithm uses similar information of product attributes to make recommendations, but products with similar attributes may have large quality differences, resulting in wrong recommendations
For example, the screenwriters of the movie Taken3 and the movie Taken are the same as many actors, so they are similar in terms of attributes, but users on the IMDB website have high scores for Taken, but not high scores for Taken3, so Take3 is recommended It is probably a wrong recommendation for users who like Taken

Method used

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  • A recommendation method based on active learning to solve commodity cold start problem
  • A recommendation method based on active learning to solve commodity cold start problem
  • A recommendation method based on active learning to solve commodity cold start problem

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Embodiment Construction

[0065] The present invention will be further described in detail below with reference to the accompanying drawings and taking the Movielens-IMDB data set as an example. The Movielens-IMDB dataset is a movie dataset, which contains historical rating data and movie attribute data (such as directors, actors, etc.) for movies.

[0066] Table 1 is the statistics of this dataset. We randomly selected 8,000 movies, and used the attributes and rating data of these movies to train the model to predict the ratings of the remaining 1,998 movies. The data of the first 8000 movies is called the training set, and the data of the last 1998 movies is called the test set.

[0067] Table 1

[0068]

[0069]

[0070] Such as figure 1 As shown, the recommendation method based on active learning to solve the commodity cold start problem includes active learning phase and prediction phase. The active learning phase includes steps 1 to 4, and the prediction phase includes step 5. The spec...

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Abstract

The invention discloses an active learning-based recommendation method for solving a commodity cold-start problem. The method comprises the steps of 1, building a scoring model of a user for a commodity, and performing pre-training on the model through historical scoring data of the user for the commodity and attribute characteristics of the commodity; 2, for a new commodity, estimating whether different users perform scoring on the commodity or not and how many points the users give by using the scoring model in the step 1; 3, selecting the users to perform scoring on the new commodity according to a result in the step 2 so as to obtain scoring data of the new commodity; 4, performing re-training on the scoring model in the step 1 by utilizing the scoring data of the new commodity; and 5, predicting scores of unselected users for the new commodity by utilizing the re-trained scoring model, and performing commodity recommendation according to the scores. According to the method, user experience of all users is considered simultaneously, so that the fairness of a selection policy is ensured to a certain extent; finite user resources are fully utilized; and the commodity is effectively recommended to the user.

Description

technical field [0001] The invention relates to the field of recommendation systems, in particular to a recommendation method based on active learning to solve the cold start problem of commodities. Background technique [0002] The rapid development of Internet multimedia has produced a large amount of information. On the one hand, it meets the needs of users for information, but on the other hand, it is difficult for users to obtain useful content from a large amount of information (information overload), so it also reduces the user's demand for information. usage efficiency. Recommender systems are a useful approach to address the problem of information overload. It predicts the user's information needs by analyzing data such as the user's historical behavior, thereby directly recommending the information that the user may need to the user. [0003] At present, the recommendation system has been widely used in the recommendation applications of commodities, movies, musi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/50
CPCG06Q30/0201
Inventor 祝宇林靖豪何石弼王北斗管子玉蔡登
Owner ZHEJIANG UNIV
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