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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許永真 | |
| dc.contributor.author | Yu-Hsuan Lin | en |
| dc.contributor.author | 林友宣 | zh_TW |
| dc.date.accessioned | 2021-06-13T03:15:34Z | - |
| dc.date.available | 2006-08-09 | |
| dc.date.copyright | 2006-08-09 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-07-31 | |
| dc.identifier.citation | [1] K. Ali and W. V. Stam. Tivo: making show recommendations using a dis-
tributed collaborative ‾ltering architecture. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004. [2] M Anderson, M. Ball, H. Boley, S. Greene, N. Howse, D. Lemire, and S. Mc- Grath. Raco‾: A rule-applying collaborative ‾ltering system. In Proceedings of COLA'03, 2003. [3] F. A. Asnicar and C. Tasso. ifweb: a prototype of user model-based intelligent agent for document ‾ltering and navigation in the world wide web. In UM97 Workshop on 'Adaptive Systems and User Modeling on the World Wide Web' Sixth International Conference on User Modeling, 1997. [4] M. Balabanovi and Y. Shoham. Fab: content-based, collaborative recommen- dation. Commun. ACM, 40:66{72, 1997. [5] D. Billsus and M. Pazzani. Newsdude: a hybrid user model for news classi‾ca- tion. In Proceedings of the Seventh International Conference on User Modeling, 1999. [6] M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative ‾lters in an online newspaper. In Proceedings of ACM SIGIR Workshop on Recommender Systems, 1999. [7] Y. Ding, X. Li, and M.E. Orlowska. Recency-based collaborative ‾ltering. In Proceedings of Seventeenth Australasian Database Conference (ADC2006), 2006. [8] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative ‾ltering to weave an information tapestry. Commun. ACM, 35:61{70, 1992. [9] J. L. Herlocker, J. A. Konstan, and J. Riedl. Explaining collaborative ‾ltering recommendations. In Computer Supported Cooperative Work, 2000. [10] G. Karypis. Evaluation of item-based top-n recommendation algorithms. Tech- nical report, CS-TR-00-46, Computer Science Dept., University of Minnesota., 2000. [11] I. Koychev. Gradual forgetting for adaptation to concept drift. In Proceedings of ECAI 2000 Workshop Current Issues in Spatio-Temporal Reasoning, 2000. [12] B. Krulwich. Lifestyle ‾nder: Intelligent user pro‾ling using large-scale demo- graphic data. AI Magazine, 18:37{45, 1997. [13] K. Lang. NewsWeeder: learning to ‾lter netnews. In Proceedings of the 12th International Conference on Machine Learning. Morgan Kaufmann publishers Inc.: San Mateo, CA, USA, 1995. [14] H. Lieberman, N. van Dyke, and A. Vivacqua. Let's browse: a collaborative web browsing agent. In USENIX Winter 1995 Technical Conference, 1999. [15] G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to- item collaborative ‾ltering. In IEEE Computer Society, volume 07, pages 76{80. IEEE Computer Society, 2003. [16] H. Liu and P. Maes. Interestmap: Harvesting social network pro‾les for rec- ommendations. In IUI Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research, 2005. [17] B. N. Miller, J. A. Konstan, and J. Riedl. Pocketlens: Toward a personal recommender system. ACM Trans. Inf. Syst., 22:437{476, 2004. [18] E. T. Oka. Vineyard: A collaborative ‾ltering service platform in distributed, 2003. [19] M. Papagelis, I. Rousidis, D. Plexousakis, and E. Theoharopoulos. Incremental collaborative ‾ltering for highly-scalable recommendation algorithms, 2005. [20] J. Pouwelse, M. V. Slobbe, J. Wang, M. J. T. Reinders, and H. Sips. P2p- based pvr recommendation using friends, taste buddies and superpeers. In Proceedings of Beyond Personalizaion 2005, the Workshop on the Next Sage of Recommender Systems Research (IUI2005), 2005. [21] P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl. Grouplens: An open architecture for collaborative ‾ltering of netnews. In Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, pages 175{186. ACM, 1994. [22] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative ‾l- tering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference (WWW10), 2001. [23] J. B. Schafer, J. A. Konstan, and J. Riedl. E-commerce recommendation appli- cations. Data Mining and Knowledge Discovery, 5:115{153, 2001. [24] U. Shardanand and P. Maes. Social information ‾ltering: Algorithms for au- tomating word of mouth'. In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, 1995. [25] B. Sheth and P. Maes. Newt: Evolving agents for personalized information ‾ltering. In Proceedings of the 9th Conference on Arti‾cial Intelligence for Applications (CAIA-93), 1993. [26] Y. Y. Shih and D. R. Liu. Hybrid recommendation approaches: Collabora- tive ‾ltering via valuable content information. In Proceedings of the Proceed- ings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 8. IEEE Computer Society, 2005. [27] A. Stefani and C. Strapparava. Personalizing access to web sites: The siteif project. In Proceedings of 2nd Workshop on Adaptive Hypertext and Hypermedia HYPERTEXT'98, 1998. [28] K. Swearingen and R. Sinha. Beyond algorithms: An hci perspective on recom- mender systems. In ACM SIGIR 2001 Workshop on Recommender Systems, 2001. [29] T. Y. Tang, P. Winoto, and K. C. C. Chan. On the temporal analysis for improved hybrid recommendations. In Proceedings of the IEEE/WIC Interna- tional Conference on Web Intelligence. IEEE Computer Society, 2003. [30] L. Ungar and D. Foster. Clustering methods for collaborative ‾ltering. In Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park California, 1998. [31] M. Vozalis and K. G. Margaritis. On the combination of collaborative ‾ltering and item-based ‾ltering. University of Macedonia, Dept. of Applied Informatics Parallel Distributed Processing Laboratory. [32] M. Wheatley. Je® bezos takes everything personally. CIO Magazine, 8 2000. [33] B. Xie, P. Han, and R. Shen. Pipecf: a scalable dht-based collaborative ‾ltering recommendation system. In Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, 2004. [34] T. Yan and H. Garcia-Molina. Sift { a tool for wide-area information dissemi- nation. In USENIX Winter 1995 Technical Conference, 1995. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/31599 | - |
| dc.description.abstract | Nowadays the amount of information in the world is increasing far more quickly than our ability to process them. How people can use their limited time to get interesting information has become an important issue in our daily life. Collabora-tive ‾ltering recommender system is one of the prevailing approaches that can help users to ‾lter unsuitable information. However, traditional collaborative ‾ltering recommender systems do not take the changing behavior of each user's interests into account. This research proposes a new time-weighted collaborative ‾ltering recommender system to capture each user's current interests precisely. The ex-perimental results show that the time-weighted collaborative ‾ltering recommender system outperforms the traditional collaborative ‾ltering recommender system with 11.2% in recommendation accuracy. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T03:15:34Z (GMT). No. of bitstreams: 1 ntu-95-R93922122-1.pdf: 506751 bytes, checksum: cc0fb23c93bf3cf8856bfc4125454068 (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | Acknowledgments i
Abstract iii List of Figures viii List of Tables ix Chapter 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Problem De‾nition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 2 Literature Survey 7 2.1 Recommender System . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Demographic Filtering Approach . . . . . . . . . . . . . . . . . . . . 9 2.3 Content-Based Filtering Approach . . . . . . . . . . . . . . . . . . . . 10 2.3.1 Standard Keyword Matching . . . . . . . . . . . . . . . . . . . 11 2.3.2 Cosine Similarity . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.3 Classi‾cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 Collaborative Filtering Approach . . . . . . . . . . . . . . . . . . . . 12 2.4.1 Nearest Neighbors . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4.2 Item-Based Filtering . . . . . . . . . . . . . . . . . . . . . . . 15 2.4.3 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.5 Hybrid Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Chapter 3 Time-Weighted Collaborative Filtering Recommender Sys- tem 19 3.1 Recommendation Process . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Correlation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2.1 Traditional Correlation Process . . . . . . . . . . . . . . . . . 23 3.2.2 Time-Weighted Correlation Process . . . . . . . . . . . . . . . 26 3.3 Aggregation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Chapter 4 Experiments and Analysis 35 4.1 Experiment Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Experiment Evaluation and Results . . . . . . . . . . . . . . . . . . . 36 4.2.1 Experiment Evaluation . . . . . . . . . . . . . . . . . . . . . . 37 4.2.2 Sensitivity of Half-Life Scaling Parameter . . . . . . . . . . . . 37 4.2.3 Comparison with Traditional Collaborative Filtering Recom- mender System . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Chapter 5 Conclusion 41 5.1 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . . 42 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Bibliography 43 | |
| dc.language.iso | en | |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 合作過濾 | zh_TW |
| dc.subject | 時間加權 | zh_TW |
| dc.subject | recommender system | en |
| dc.subject | collaborative filtering | en |
| dc.subject | time-weighted | en |
| dc.title | 時間加權之合作過濾推薦系統 | zh_TW |
| dc.title | Time-Weighted Collaborative Filtering Recommender System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 94-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王傑智,林智仁,朱浩華 | |
| dc.subject.keyword | 合作過濾,推薦系統,時間加權, | zh_TW |
| dc.subject.keyword | collaborative filtering,time-weighted,recommender system, | en |
| dc.relation.page | 47 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2006-07-31 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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