Browse > Article

Collaborative Recommendations using Adjusted Product Hierarchy : Methodology and Evaluation  

Cho, Yoon-Ho (School of e-Business, Kookmin University)
Park, Su-Kyung (School of Business Administration, Kyung Hee University)
Ahn, Do-Hyun (School of Business Administration, Kyung Hee University)
Kim, Jae-Kyeong (School of Business Administration, Kyung Hee University)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.29, no.2, 2004 , pp. 59-75 More about this Journal
Abstract
Recommendation is a personalized information filtering technology to help customers find which products they would like to purchase. Collaborative filtering works by matching customer preferences to other customers in making recommendations. But collaborative filtering based recommendations have two major limitations, sparsity and scalability. To overcome these problems we suggest using adjusted product hierarchy, grain. This methodology focuses on dimensionality reduction and uses a marketer's specific knowledge or experience to improve recommendation quality. The qualify of recommendations using each grain is compared with others by several experimentations. Experiments present that the usage of a grain holds the promise of allowing CF-based recommendations to scale to large data sets and at the same time produces better recommendations. In addition. our methodology is proved to save the computation time by 3∼4 times compared with collaborative filtering.
Keywords
Recommendation; Collaborative Filtering; Product Hierarchy; CRM;
Citations & Related Records
연도 인용수 순위
  • Reference
1 The description identification problem /
[ Mellish,C. ] / Artificial Intelligence   DOI   ScienceOn
2 Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases /
[ Han,J.;Fu,Y. ] / AAAI 94 Workshop on Knowledge Discovery in Databases
3 Learning collaborative information filters /
[ Billsus,D.;Pazzani,M. ] / Proceedings of Recommender Systems Workshop. Tech. Report WS-98-08
4 Systemic classification and its effciency /
[ Brew,C. ] / Computational Linguistics
5 A Personalized Recommender System based on Web Usage Mining and Decision Tree Induction /
[ Cho,Y.H.;Kim,J.K.;Kim,S.H. ] / Expert Systems with Applications   DOI   ScienceOn
6 Datadriven discovery of quantitative rules in relational databases /
[ Han,J.;Cai,Y.;Cercone,N. ] / IEEE Tran. on Knoweledge and Data Engineeering   DOI   ScienceOn
7 A Personalized Recommendation Procedure for Internet Shopping Support /
[ Kim,J.K.;Cho,Y.H.;Kim,W.J.;Kim,J.R.;Suh,J.H. ] / Electronic Commerce Research and Applications   DOI   ScienceOn
8 GroupLens : An Open Architecture for Collaborative Filtering of Netnews /
[ Resnick,P.;Iacovou,N.;Suchak,M.;Bergstrom,P.;Riedl,J. ] / Proceedings of CSCW 94
9 Expert-driven validation of rule-based user models in personalization applications /
[ Adomavicius,G.;Tuzhilin,A. ] / Data Mining and Knowledge Discovery   DOI
10 Application of Dimensionality Reduction in Recommender System A Case Study /
[ Sarwar,B.;Karypis,G.;Konstan,J.;Riedl,J. ] / Proceeding of the Tenth International World Wide Web Conference
11 Indexing by Latent Semantic Analysis /
[ Deerwester,S.;Dumais,S.T.;Furnas,G.W.;Landauer,T.K.;Harshman,R. ] / Journal of the American Society for Information Science   DOI
12 Collaborative Filtering to Weave an Information Tapestry /
[ Goldberg,D.;Nichols,D.;Oki,B.M.;Terry,D. ] / Communications of the ACM
13 /
[ Fayyad,U.M.;Piatetsky-Shapiro,G.;Smyth,P.;Uthurusamy,R. ] / Advances in Knowledge Discovery and Data Mining
14 Item-based collaborative filtering recommendation algorithm /
[ Sarwar,B.;Karypis,G.;Konstan,J.;Ridel,J. ] / Proceeding of the Tenth International World Wide Web Conference
15 Recommending and Evaluating Choices in a Virtual Community of Use /
[ Hill,W.;Stead,L.;Rosenstein,M.;Furnas,G. ] / Proceedings of CHI 95
16 Recommender Systems /
[ Resnick,P.;Varian,H.R. ] / Special issue of Communications of the ACM
17 Analysis of recommendation algorithms for e-commerce /
[ Sarwar,B.;Karypis,G.;Konstan,J.;Riedl,J. ] / Proceedings of ACM E-Commerce 2000 Conference
18 GroupLens : Applying collaborative filtering to usenet news /
[ Konstan,J.;Miller,B.;Maltz,D.;Herlocker,J.;Gordon,L.;Riedl,J. ] / Communications of the ACM   DOI   ScienceOn
19 Combining Collaborative Filtering WIith Personal Agents for Better Recommendations /
[ Good,N.;Schafer,B.;Konstan,J.;Borchers,A.;Sarwar,B.;Herlocker,J.;Ridel,J. ] / Proceedings of the AAAI-99 conference
20 Using Web Usage Mining and SVD to Improve E-commerce Recommendation Quality /
[ Kim,J.K.;Cho,Y.H. ] / Lecture Notes in Artificial Intelligence 2891
21 Evaluation of item-based top-n recommendation algorithms /
[ Karypis,G. ] / Communications of the ACM
22 /
[ Lu,Y. ] / Concept hierarchy in data mining : specification, generation and implementation
23 /
[ Berry,J.A.;Linoff,G. ] / Data mining techniques : for marketing sales, and customer support
24 A Tree Projection Algroithm for Generation of Frequent Itemsets /
[ Agarwal,R.C.;Aggarwal,C.;Prasad,V. ] / Journal of Parallel and Distributed Computing