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完整後設資料紀錄
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-
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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.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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