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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦 | |
| dc.contributor.author | Meng Lee | en |
| dc.contributor.author | 李孟 | zh_TW |
| dc.date.accessioned | 2021-06-15T13:43:57Z | - |
| dc.date.available | 2016-02-02 | |
| dc.date.copyright | 2016-02-02 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-12-14 | |
| dc.identifier.citation | Bawden, D., and Robinson, L. 2009. 'The Dark Side of Information: Overload, Anxiety and Other Paradoxes and Pathologies,' Journal of information science (35:2), pp. 180-191.
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'A Random Walk Method for Alleviating the Sparsity Problem in Collaborative Filtering,' Proceedings of the 2008 ACM conference on Recommender systems: ACM, pp. 131-138. Zheng, N., Li, Q., Liao, S., and Zhang, L. 2010. 'Which Photo Groups Should I Choose? A Comparative Study of Recommendation Algorithms in Flickr,' Journal of Information Science (36:6), pp. 733-750. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51673 | - |
| dc.description.abstract | Nowadays, the status of social networking sites become more and more important in people’s life. Many social networking sites encourage users to create their own communities or join other’s communities to interact with other users, but there are information overload problem that users can’t easily find the communities they want to join. And this may pull users back from using the social service.
In this paper, we propose a useful community recommendation approach that combine MF and LTR to model user and community’s preference, and we also incorporate both social information and user-community interactive degree in our method. The result by using a real-world dataset shows that both LTR and social information can help enhance recommendation quality evaluated by coverage and nDCG. We also show that when training pairwise learning to rank model, the recommendation quality can be further improved if one choose the trained pairs wisely. We compare some possible pair selection strategies and found that the most important thing for these pair selections is to recognize the preferable communities for a user. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T13:43:57Z (GMT). No. of bitstreams: 1 ntu-104-R02725032-1.pdf: 2374958 bytes, checksum: 6c53b839e79759f48a570de2ac07a55a (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii 目錄 iv LIST OF FIGURES v LIST OF TABLES vi 1 INTRODUCTION 1 2 RELATED WORK 3 2.1 Collaborative Filtering 3 2.2 Learning to Rank 5 2.3 Community Recommendation 6 3 OUR APPROACH 7 3.1 User-Community Interaction 8 3.2 Quality Pair Selection 8 3.3 Matrix Factorization 10 3.4 Social Factorization 11 3.5 Pairwise Learning with Social Factor 12 4 EXPERIMENT 13 4.1 Data set and Evaluation metric 14 4.2 Parameter Settings 17 4.3 Comparison of Different Pair Selection Strategies 18 4.4 Performance Comparison 20 5 CONCLUSION 24 REFERENCES 25 | |
| dc.language.iso | en | |
| dc.subject | 社交影響 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 成對學習 | zh_TW |
| dc.subject | 社團推薦 | zh_TW |
| dc.subject | 使用者-社團互動 | zh_TW |
| dc.subject | community recommendation | en |
| dc.subject | recommender system | en |
| dc.subject | Learning to rank | en |
| dc.subject | social influence | en |
| dc.subject | pairwise learning | en |
| dc.title | 利用社交資訊及使用者社團互動程度之成對學習社團推薦方法 | zh_TW |
| dc.title | Pairwise Learning for Coummunity Recommendation Utilizing Social Information and User-Community Interaction Degree | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張嘉惠,陳孟彰,蔡銘峰 | |
| dc.subject.keyword | 成對學習,推薦系統,社團推薦,使用者-社團互動,社交影響, | zh_TW |
| dc.subject.keyword | Learning to rank,pairwise learning,recommender system,social influence,community recommendation, | en |
| dc.relation.page | 26 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-12-15 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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