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Negative sample selection method against single-type collaborative filtering problems

A collaborative filtering and negative sample technology, applied in the field of Internet recommendation, can solve the problems of not considering the influence of the user's social relationship interests, not considering the user's interest characteristics, etc.

Active Publication Date: 2017-12-01
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The problem with these negative sample selection methods is that they basically select items randomly, without considering the user’s own interest characteristics, nor the influence of the user’s social relationship on their interests

Method used

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  • Negative sample selection method against single-type collaborative filtering problems
  • Negative sample selection method against single-type collaborative filtering problems
  • Negative sample selection method against single-type collaborative filtering problems

Examples

Experimental program
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Embodiment

[0063] Assume that a user set {a, b, c, d, e, f} consisting of 6 users acts on an item set {item1, item2,..., item10} consisting of 10 items. User behavior data records are shown in Table 1. The social relationship here is assumed to be a one-way follow relationship. For example, user a follows user b, then b is a friend of a, but a is not a friend of b. In this embodiment, the specific process of selecting negative samples for user a is introduced in detail. Figure 6 It is a schematic diagram of friends obtained according to the friends following relationship in this embodiment.

[0064] Table 1 User Behavior Data

[0065] user

Items with behavior

follow friends

a

item2, item3, item5

b,c,d

b

item1, item2, item7

a,d

c

item1, item2, item3, item4, item7

a

d

item1, item2, item5, item6

b

e

item1, item5, item7, item9

a,d,c

f

item4, item8, item10, item1

e

[0066] Step 1: ...

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Abstract

The invention provides a negative sample selection method against single-type collaborative filtering problems. The method aims to help selection of a negative sample of each user according to an implicit feedback scene without an explicit negative sample in a recommendation algorithm based on a machine learning model. The method comprises the specific steps that (1) weights of articles selected to serve as negative samples are calculated based on the popularity of the articles; (2) weights of the articles selected to serve as the negative samples are calculated based on the social relation between the users; (3) weights of the articles selected to serve as the negative samples are calculated based on preferences of the users on article features; (4) the popularity weights, the social relation weights and the feature weights of the articles are fused to calculate the probability that the articles are selected to serve as the negative samples of a target user; and (5) a plurality of articles with the highest negative sample probability are selected to serve as the negative samples of the target user according to a certain proportion and the number of positive samples of the target user.

Description

technical field [0001] The invention belongs to the technical field of Internet recommendation, and in particular relates to a negative sample selection method in an unclear negative sample scenario. Background technique [0002] With the explosive growth of Internet data, how to extract effective information from massive data and provide it to users is an urgent problem to be solved in current Internet applications. Personalized information recommendation is an effective way to solve this problem. It tracks the user's historical behavior, extracts the user's interest characteristics, and analyzes the matching degree between the information and the user's characteristics, so as to recommend information that may be of interest to the user. At present, personalized information recommendation has been widely adopted by major Internet platforms as an important tool to enhance user stickiness and increase platform revenue. For example, JD.com recommends products to users; NetEas...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q30/02
CPCG06F16/9535G06Q30/0251G06Q30/0255
Inventor 刘梦娟马小栓薛浩
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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