| 講演名 | 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(会津大), Incheon Paik(会津大), |
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| 抄録(和) | 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. |
| 抄録(英) | 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. |
| キーワード(和) | Recommendation / Collaborative Filtering / Web Services / Ontology Learning / Cold-Start / Sparsity |
| キーワード(英) | Recommendation / Collaborative Filtering / Web Services / Ontology Learning / Cold-Start / Sparsity |
| 資料番号 | KBSE2018-30,SC2018-25 |
| 発行日 | 2018-11-02 (KBSE, SC) |
| 研究会情報 | |
| 研究会 | KBSE / SC |
|---|---|
| 開催期間 | 2018/11/9(から2日開催) |
| 開催地（和） | 神戸大学 瀧川記念学術交流会館 |
| 開催地（英） | |
| テーマ（和） | 「ソフトウェア／サービスとAI」, 一般 |
| テーマ（英） | |
| 委員長氏名（和） | 粂野 文洋(日本工大) / 中村 匡秀(神戸大) |
| 委員長氏名（英） | Fumihiro Kumeno(Nippon Inst. of Tech.) / Masahide Nakamura(Kobe Univ.) |
| 副委員長氏名（和） | 中川 博之(阪大) / 菊地 伸治(会津大) / 山登 庸次(NTT) |
| 副委員長氏名（英） | Hiroyuki Nakagawa(Osaka Univ.) / Shinji Kikuchi(Univ. of Aizu) / Yoji Yamato(NTT) |
| 幹事氏名（和） | 猿渡 卓也(NTT) / 木村 功作(富士通研) / 細野 繁(NEC) / 木村 功作(富士通研) |
| 幹事氏名（英） | Takuya Saruwatari(NTT) / Kosaku Kimura(Fujitsu labs.) / Shigeru Hosono(NEC) / Kosaku Kimura(Fujitsu Lab.) |
| 幹事補佐氏名（和） | 高橋 竜一(茨城大) / 田辺 良則(鶴見大) |
| 幹事補佐氏名（英） | Ryuichi Takahashi(Ibaraki Univ.) / Yoshinori Tanabe(Tsurumi Univ.) |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Knowledge-Based Software Engineering / Technical Committee on Service Computing |
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| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Evaluation of the Effectiveness of Recommendation while Managing the Data Density of the Web Service-User Preference |
| サブタイトル（和） | |
| キーワード(1)（和/英） | Recommendation / Recommendation |
| キーワード(2)（和/英） | Collaborative Filtering / Collaborative Filtering |
| キーワード(3)（和/英） | Web Services / Web Services |
| キーワード(4)（和/英） | Ontology Learning / Ontology Learning |
| キーワード(5)（和/英） | Cold-Start / Cold-Start |
| キーワード(6)（和/英） | Sparsity / Sparsity |
| 第 1 著者 氏名（和/英） | Rupasingha Arachchilage Hiruni Madhusha Rupasingha / Rupasingha Arachchilage Hiruni Madhusha Rupasingha |
| 第 1 著者 所属（和/英） | University of Aizu(略称：会津大) University of Aizu(略称：UOA) |
| 第 2 著者 氏名（和/英） | Incheon Paik / Incheon Paik |
| 第 2 著者 所属（和/英） | University of Aizu(略称：会津大) University of Aizu(略称：UOA) |
| 発表年月日 | 2018-11-09 |
| 資料番号 | KBSE2018-30,SC2018-25 |
| 巻番号（vol） | vol.118 |
| 号番号（no） | KBSE-292,SC-293 |
| ページ範囲 | pp.13-18(KBSE), pp.13-18(SC), |
| ページ数 | 6 |
| 発行日 | 2018-11-02 (KBSE, SC) |