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Top-k recommendation method based on neighborhood

A top-k and recommendation method technology, applied in the computer field, can solve problems such as being unable to respond to implicit feedback, and achieve the effect of increasing accuracy

Active Publication Date: 2013-10-30
杭州北岱科技有限公司
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AI Technical Summary

Problems solved by technology

[0021] The purpose of the present invention is to provide a new neighborhood-based recommendation method for the above-mentioned existing neighborhood-based recommendation method that cannot respond to implicit feedback. purpose of precision

Method used

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  • Top-k recommendation method based on neighborhood
  • Top-k recommendation method based on neighborhood
  • Top-k recommendation method based on neighborhood

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Embodiment Construction

[0090] Combine below figure 1 , further describe in detail the more preferred hybrid neighborhood-based recommendation method among the above three solutions of the present invention.

[0091] Step 1: Establish a user-object relationship matrix.

[0092] Obtain a set of users u including the target user from the server as the user set U, and the number of users is marked as u 0 , and then obtain a group of recommended objects i as the object set I;

[0093] Read from the server the behavior data generated by each user in the user set U for each recommended object in the object set;

[0094] When any user does not generate behavior data for any recommended object, a preset value r m as hypothetical behavioral data;

[0095] Establish a user-object relationship matrix according to the acquired behavioral data values ​​and assumed behavioral data values;

[0096] In this matrix, the behavior data or assumed behavior data generated by the same user for different recommended o...

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Abstract

The invention belongs to the technical field of computers, relates to a personalized recommendation technology finished by a computer and discloses three recommendation methods based on the neighborhood. The method includes that attributes of users are analyzed through modeling of interests and hobbies of the users, then similar user groups are found for target users, and objects which the users may be interested in are recommended for the users. During object recommendation, the method considers observed behavior data such as scoring and purchasing information, and also considers missed scoring information, namely hidden feedback information. Meanwhile, the method fully uses similarity of the interests and the hobbies of the users in social networks to model the interests of the users, considers effects of hidden feedback on results and effectively improves the recommendation precision.

Description

technical field [0001] The invention belongs to the technical field of computers, and relates to a personalized recommendation technology completed by a computer, in particular to a neighborhood-based top-k recommendation method. Background technique [0002] Personalized recommendation is becoming more and more important in our daily life, especially the emergence of web2.0 brings massive data. Accurate recommendations can help users easily find relevant products and save users the time of searching in massive data. Today's e-commerce manufacturers and companies that use Internet advertising as their income have invested a lot of manpower and material resources in intelligent personalized recommendations. Since the mid-1990s, the field of personalized recommendation has become a very important field of scientific research. The most commonly used method in the recommendation system is the collaborative filtering method-only relying on the user's historical behavior records...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q30/02
Inventor 杨希旺陈飞飞
Owner 杭州北岱科技有限公司
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