Recommendation System for Large Website Based on Enhanced Association Rules Mining

doi:doi: 10.3969/j.issn.1006-2475.2016.10.007

Abstract

Abstract:

Aiming at the problem that the content based recommendation system has high delay and low degree of satisfaction of recommendation in the large web site, an enhanced

association rules mining based recommendation system for large web site is presented. Firstly, the items with low frequency occurrence are eliminated so that the database is

minimized; Then, a counter is introduced to each transaction, and the counter is used to count the number of replication of the transaction instead of the directly clustering

process of repeated transactions in the traditional recommendation system, this improved strategy not only improves the efficiency of recommendation but also the scalability of

the recommendation engine. Real data based experiments results show that the proposed system is real time and has high recommendation accuracy.

Key words: association rules, recommendation system, real time, transaction counter, recommendation engine

CLC Number:

TP301

ZOU Yu， XIAO Qian， WU Shurong. Recommendation System for Large Website Based on Enhanced Association Rules Mining[J]. Computer and Modernization, doi: doi: 10.3969/j.issn.1006-2475.2016.10.007.

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Recommendation System for Large Website Based on Enhanced Association Rules Mining

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