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Item recommendation system

A project recommendation and project technology, applied in the field of recommendation, can solve the problems of low unpurchased books, unable to recommend similarity, etc., to achieve high efficiency

Inactive Publication Date: 2008-11-26
HITACHI LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when a set of keywords of a certain type is used as a set of keywords of another type in a content-based recommendation system, although unpurchased books with a high degree of similarity can be recommended, books with a low degree of similarity cannot be recommended. of unpurchased books

Method used

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  • Item recommendation system
  • Item recommendation system
  • Item recommendation system

Examples

Experimental program
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Embodiment Construction

[0046] Items that may be surprising to the user can be recommended by randomly adding and deleting items from the recommendation keyword set. However, when keywords are deleted or added casually or randomly, the efficiency of recommendation is poor. Therefore, a change rule for adding and deleting keywords described in the action unit is used based on keywords, user profiles, genres, etc. included in the recommendation keywords described in the status unit. When the user chooses to use the item recommended by the change rule or gives a high evaluation (evaluation), the weight given to the change rule increases, so the possibility of applying the change rule next time increases, and the application is not likely to be the change rule. reduced sex. Due to the common use of the change rule set by users, the change rule that contributes to the generation of the item selected by a certain user is more likely to be used in the item recommendation of other users with a similar profi...

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Abstract

To recommend an item which is highly unexpected to a user because its similarity to user preferences is low and which is useful to the user. A rule that modifies a set of keywords for recommending an item is randomly applied, and a keyword which a user does not prefer is added and a keyword which a user prefers is removed, and then this recommendation result is mixed with a recommendation result of a set of keywords before the above modification, and the mixed result is presented to a user and at the same time the application probability of a rule is learned on the basis of the user's evaluation to a recommended item.

Description

technical field [0001] The present invention relates to a recommendation (Recommendation) technology in the field of artificial intelligence that facilitates users to purchase commodities and audio-visual programs. Background technique [0002] There are two methods for recommending items such as products and programs. One is a method of recommending similar items, in which similar items are recommended to users using a set of keywords that characterize the items. They are generally called content-based item recommendation (Contents-Based Recommendation). The other is to recommend non-similar items, without using a collection of keywords, and recommending items that may not be similar to users. As a representative method, there is a method called collaborative filtering (Collaboration Filtering). In this method, items are recommended using the selection tendencies of the user to be recommended and people whose selection tendencies are similar to the item. [0003] In the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q30/00G06Q30/02G06Q30/06G06Q50/00
CPCH04N21/4332H04N21/44222H04N21/4532H04N21/4668H04N21/4667H04N21/44224
Inventor 竹内胜
Owner HITACHI LTD
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