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A Collaborative Filtering Recommendation Method Based on Hierarchical Items

A collaborative filtering recommendation and hierarchical technology, which is applied in the fields of instruments, calculations, and electrical digital data processing, can solve problems such as inability to make full use of content data, cold-start data, and sparsity, so as to alleviate data sparsity and cold-start problems, and improve Effects of Satisfaction, Increased Accuracy, and Diversity

Active Publication Date: 2021-09-28
HANGZHOU DIANZI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, existing collaborative filtering methods cannot make full use of content data, and face problems such as cold start and data sparsity.

Method used

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  • A Collaborative Filtering Recommendation Method Based on Hierarchical Items
  • A Collaborative Filtering Recommendation Method Based on Hierarchical Items
  • A Collaborative Filtering Recommendation Method Based on Hierarchical Items

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Embodiment Construction

[0030] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0031] The present invention based on hierarchical item collaborative filtering recommendation method includes the following steps:

[0032] (1). Collect behavioral data of all users U={u 1 ,u 2 ,...,u |U|} is the set of all users, and the historical behavior data of user u∈U is H u ={(i 1 ,t 1 ),(i 2 ,t 2 ),…,(i m ,t m )}, I={i 1 ,i 2 ,...,i |M|} is the collection of all items. Collect metadata M of all items in I, including but not limited to categories, attributes, tags and other information.

[0033] (2). The behavior data of each user is divided into different sessions according to time, and the behavior data with close time is divided into one session. Taking user u∈U as an example, its historical behavior data can be divided into ...

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Abstract

The invention discloses a hierarchical item-based collaborative filtering recommendation method, comprising the following steps: collecting user behavior data on items and item metadata; modeling the collected behavior data and item metadata and calculating item similarity ; Recommendations based on hierarchical item collaborative filtering. The present invention mainly uses a variety of data, including user behavior data on items and metadata information of items, to calculate the similarity between items and implement recommendation based on collaborative filtering of hierarchical items, so as to alleviate data sparsity and cold start problems , and then improve the accuracy and diversity of the recommendation results.

Description

technical field [0001] The invention belongs to the technical field of data mining and recommendation, and in particular relates to a hierarchical item-based collaborative filtering recommendation method. Background technique [0002] Recommender systems can help users find relevant items from massive online content to reduce search costs, and accurate calculation of item similarity is one of the cores of realizing personalized recommendation systems. However, traditional methods usually suffer from problems such as low recommendation accuracy and similar recommendation effects, and cannot meet users' needs. The collaborative filtering recommendation algorithm can use the behavioral data between users and items to implement recommendations, which improves the accuracy of recommendations and user satisfaction to a certain extent. However, existing collaborative filtering methods cannot make full use of content data and face problems such as cold start and data sparsity. The...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9536
CPCG06F16/9536
Inventor 张新王东京俞东进
Owner HANGZHOU DIANZI UNIV