Recommendation System with Biclustering  

作　　者：Jianjun Sun Yu Zhang 

机构地区：[1]School of Computer Science,Northwestern Polytechnical University,Xi’an 710072,China [2]Ningbo Institute of Northwestern Polytechnical University,Ningbo 315103,China

出　　处：《Big Data Mining and Analytics》2022年第4期282-293,共12页大数据挖掘与分析（英文）

基　　金：This work was supported by Ningbo Natural Science Foundation(No.202003N4057);the National Natural Science Foundation of China(Nos.62172336 and 62032018).

摘　　要：The massive growth of online commercial data has raised the request for an automatic recommender system to benefit both users and merchants.One of the most frequently used recommendation methods is collaborative filtering,but its accuracy is limited by the sparsity of the rating dataset.Most existing collaborative filtering methods consider all features when calculating user/item similarity and ignore much local information.In collaborative filtering,selecting neighbors and determining users’similarities are the most important parts.For the selection of better neighbors,this study proposes a novel biclustering method based on modified fuzzy adaptive resonance theory.To reflect the similarity between users,a new measure that considers the effect of the number of users’common items is proposed.Specifically,the proposed novel biclustering method is first adopted to obtain local similarity and local prediction.Second,item-based collaborative filtering is used to generate global predictions.Finally,the two resultant predictions are fused to obtain a final one.Experiment results demonstrate that the proposed method outperforms state-of-the-art models in terms of several aspects on three benchmark datasets.

关 键 词：Recommendation System(RS) Collaborative Filtering(CF) local pattern BICLUSTERING similarity measure 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]