Presentation 2018-11-09
Evaluation of the Effectiveness of Recommendation while Managing the Data Density of the Web Service-User Preference
Rupasingha Arachchilage Hiruni Madhusha Rupasingha, Incheon Paik,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Recommender systems become important in the research and commercial society, where many recommendationsolutions have been suggested for the providing predictions. These solutions typically perform differently in various methodsand datasets. In this paper, we deal with Web service recommendation to discover applicable services quickly and accuratelyusing a collaborative filtering (CF) technique which suffers from data sparsity and cold-start problems. We manage the densityof Web service-user preference using a novel ontology-based clustering approach that used domain specificity and servicesimilarity for the ontology generation. Using an evaluation we identified this approach can easily and effectively increase thedata density of the user-service dataset by the deal with non-rated user preferences based on the user?s past preferred domains. Then user ratings are predicted based on the trust value between users by calculating the correlation of users. Evaluations arebased on the different sparsity alleviating methods and ontology generation, and it shows proposed method effectively andefficiently reaches lower prediction error comparing with existing approaches.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Recommendation / Collaborative Filtering / Web Services / Ontology Learning / Cold-Start / Sparsity
Paper # KBSE2018-30,SC2018-25
Date of Issue 2018-11-02 (KBSE, SC)

Conference Information
Committee KBSE / SC
Conference Date 2018/11/9(2days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Fumihiro Kumeno(Nippon Inst. of Tech.) / Masahide Nakamura(Kobe Univ.)
Vice Chair Hiroyuki Nakagawa(Osaka Univ.) / Shinji Kikuchi(Univ. of Aizu) / Yoji Yamato(NTT)
Secretary Hiroyuki Nakagawa(NTT) / Shinji Kikuchi(Fujitsu labs.) / Yoji Yamato(NEC)
Assistant Ryuichi Takahashi(Ibaraki Univ.) / Yoshinori Tanabe(Tsurumi Univ.)

Paper Information
Registration To Technical Committee on Knowledge-Based Software Engineering / Technical Committee on Service Computing
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Evaluation of the Effectiveness of Recommendation while Managing the Data Density of the Web Service-User Preference
Sub Title (in English)
Keyword(1) Recommendation
Keyword(2) Collaborative Filtering
Keyword(3) Web Services
Keyword(4) Ontology Learning
Keyword(5) Cold-Start
Keyword(6) Sparsity
1st Author's Name Rupasingha Arachchilage Hiruni Madhusha Rupasingha
1st Author's Affiliation University of Aizu(UOA)
2nd Author's Name Incheon Paik
2nd Author's Affiliation University of Aizu(UOA)
Date 2018-11-09
Paper # KBSE2018-30,SC2018-25
Volume (vol) vol.118
Number (no) KBSE-292,SC-293
Page pp.pp.13-18(KBSE), pp.13-18(SC),
#Pages 6
Date of Issue 2018-11-02 (KBSE, SC)