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Individualized recommendation method based on user preferences and commodity properties

A technology of product attributes and recommendation methods, which is applied in marketing and other directions, can solve problems such as failure of the recommendation system, less data preference sorting, and inability to make effective and reliable recommendations, so as to achieve the effect of improving the recall rate

Inactive Publication Date: 2014-05-28
BEIJING INSTITUTE OF TECHNOLOGYGY
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AI Technical Summary

Problems solved by technology

Because new users do not have any information in the preference ranking, in the memory-based collaborative filtering method, no recommendation can be made for new users
When new users submit their preference rankings, they expect the recommender system to make recommendations for them, but the data they submit (preference rankings) may be too small to make effective and reliable recommendations. Therefore, new users may feel that the recommended The system did not meet their expectations, thus abandoning the use of recommender systems

Method used

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  • Individualized recommendation method based on user preferences and commodity properties
  • Individualized recommendation method based on user preferences and commodity properties
  • Individualized recommendation method based on user preferences and commodity properties

Examples

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Embodiment Construction

[0027] The specific implementation of the present invention will be described in further detail below through examples.

[0028] In a certain site, there are 1,000 users and 5,000 movies, and each movie has three attributes: name, release year, and category. Now use the personalized distributed recommendation method based on the improved similarity matrix to the first one in the site. The user recommends items, and the specific process is as follows: figure 1 As shown, the operation steps are as follows:

[0029] According to step 1: determine the item-based similarity matrix;

[0030] Define the feature vector of the movie: item i =(p 1 ,p 2 ,,p 3 ), p i (1≤i≤3) represents the value of the i-th feature of this item. First, each movie is represented by a 3-dimensional vector item i =(w 1 ,w 2 ,,w 3 ), where w i (1≤i≤3) represents the value of the i-th feature of the item. Then by computing the distance A between the vectors representing the items ij to represent ...

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Abstract

The invention relates to an individualized recommendation method based on user preferences and commodity properties, belonging to the field of machine learning. The individualized recommendation method comprises the following steps of adding attribute information of a project and doing recommendation by commodity property information when preference information does not exist. Meanwhile, the recall ratio of a recommendation system is improved by the recommendation method. With the adoption of the individualized recommendation method based on the user preferences and the commodity properties, the problem of cold starting based on a new project is solved.

Description

technical field [0001] The invention relates to a personalized recommendation method based on user preferences and commodity attributes, belonging to the field of machine learning. Background technique [0002] With the further development of intelligent computing and e-commerce technology, research on collaborative filtering recommendation system has become a hot topic in the field of e-commerce. Collaborative filtering technology is an important technology in e-commerce. The research on collaborative filtering recommendation technology is mainly in the Internet mode, from user level, item rating, content filtering, contextual information integration, cluster analysis, association rule from an analysis perspective. The basic idea can be summarized as finding the nearest neighbor users who have the same or similar user behavior as the target user while ensuring enough scoring data, and selecting the product with the highest user behavior similarity as a recommendation list ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02
Inventor 宿红毅王彩群闫波郑宏
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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