|Table of Contents|

An Improved Unifying Tags and Ratings Collaborative Filteringfor Recommendation System(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2015年01期
Page:
98-
Research Field:
计算机科学
Publishing date:

Info

Title:
An Improved Unifying Tags and Ratings Collaborative Filteringfor Recommendation System
Author(s):
Gao Na; Yang Ming
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
Collaborative Filtering; tag; recommendation system; sparsity
PACS:
TP391
DOI:
-
Abstract:
The recommendation system has become the hot topic widely studied in the field of big data due to the massive amounts of data it contains. While the collaborative filtering algorithm is one of the most popular approach in the recommendation system. When making recommendations using the traditional collaborative filtering(CF)algorithms based on ratings matrix,we face the problem of sparsity that seriously impairs the quality of recommendation. Meanwhile,there is a large number of tags information that describe the attribute characteristics of users and items. Integrating these tags information into the traditional recommendation algorithms is a promising means to alleviate the sparsity problem. Therefore,to address the sparsity problem,this paper proposes a new collaborative filtering recommendation algorithm that integrates the tags and ratings,named UTR-CF. This algorithm utilizes the tags information and the ratings data simultaneously to compute the similarity between users or items,and then generate the recommendations. The experimental results indicate that the newly developed algorithm can alleviate the sparsity problem,and improve the accuracy of recommendation system simultaneously.

References:

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Last Update: 2015-03-30