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研究生:王珮恂
研究生(外文):Pei-Xun Wang
論文名稱:建立考量時間的個人化排序之序列性商品推薦系統
論文名稱(外文):Time-aware personalized ranking for sequential item recommendation
指導教授:林守德林守德引用關係
指導教授(外文):Shou-De Lin
口試委員:蔡銘峰李政德駱宏毅
口試委員(外文):Ming-Feng TsaiCheng-Te LiHung-Yi Lo
口試日期:2016-07-26
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:30
中文關鍵詞:序列性商品推薦時間感知馬可夫鏈矩陣分解
外文關鍵詞:sequential item recommendationtime-awareMarkov chainMatrix Factorization
相關次數:
  • 被引用被引用:0
  • 點閱點閱:245
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
本論文旨要為序列性資料建立一推薦系統。主要目標是根據使用者當前的行為來預測接下來可能的行動。為了解決這樣的問題,前人提出的FPMC模型可同時考慮序列行為及使用者偏好。我們汲取相似於FPMC模型的概念,不僅利用當前的行為,更把離目前較遠的序列行為納入考量,建造一全面性的模型。除此之外,我們衍伸FPMC模型,並加入使用者行為的時間資訊,建造一時間感知的模型。在此模型中,我們提出兩個簡單且有效的方法。第一,我們利用矩陣分解來獲取時間與物品間的潛在關係。第二,我們在BPR優化過程中,利用時間因素改進取負樣技巧。在包含音樂及課程的資料集上所做的實驗結果顯示,我們提出的方法比原先的FPMC模型來得好。

In this thesis, we aim at building a recommender system for sequential data. The goal is to predict a user’s next action based on his or her last basket of actions. In order to solve this task, FPMC is proposed by Rendle to model both sequential behavior and user preference. By utilizing similar concept of FPMC, we attempt to construct a generalized model to predict actions based on not only current actions of users but also their farther previous sequential behavior. In addition, we extend FPMC to incorporate temporal information of behavior. In our model, two simple and effective methods are introduced. First, relationship between time and items is captured by exploiting Matrix Factorization. Second, we improve negative sampling technique by taking time constraint into account for solving BPR optimization. Experimental results on two datasets, including music dataset and course dataset, show that our method outperforms state-of-the-art.

Abstract iii
Contents iv
CHAPTER 1. Introduction 1
CHAPTER 2. Related work 3
CHAPTER 3. Methodology 5
3.1 Bayesian Personalized Ranking 5
3.2 Factorizing Personalized Markov Chains 6
3.3 FPMC with several previous baskets 10
3.4 Time-aware FPMC 12
3.5 Time constraint on Time-aware FPMC 14
CHAPTER 4. data and Experiment 18
4.1 Data Description 18
4.2 Evaluation Metrics 19
4.3 Model Comparison 20
4.3.1 Most popular vs. FPMC 20
4.3.2 FPMC with M previous baskets 21
4.3.3 Time-aware Most popular vs. Time-aware FPMC 22
4.3.4 FPMC vs. Time-aware FPMC 22
4.3.5 Time-aware FPMC vs. Time constraint on Time-aware FPMC 23
4.3.6 Ensemble 23
4.4 Impact of FPMC with several previous baskets 26
CHAPTER 5. Conclusion 28
Reference 29

[1] Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010.
Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web (WWW ''10). ACM, New York, NY, USA, 811-820.
[2] Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI ''09). AUAI Press, Arlington, Virginia, United States, 452-461.
[3] B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Using sequential and non-sequential patterns in predictive web usage mining tasks. In ICDM ’02: Proceedings of the 2002 IEEE International Conference on Data Mining, page 669, Washington, DC, USA, 2002.
[4] Shuo Chen, Josh L. Moore, Douglas Turnbull, and Thorsten Joachims. 2012. Playlist prediction via metric embedding. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ''12). ACM, New York, NY, USA, 714-722.
[5] Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. 2013. Where you like to go next: successive point-of-interest recommendation. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence (IJCAI ''13), Francesca Rossi (Ed.). AAAI Press 2605-2611.
[6] Rong Pan, Yunhong Zhou, Bin Cao, Nathan N. Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-Class Collaborative Filtering. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (ICDM ''08). IEEE Computer Society, Washington, DC, USA, 502-511.
[7] Xiang Wu, Qi Liu, Enhong Chen, Liang He, Jingsong Lv, Can Cao, and Guoping Hu. 2013. Personalized next-song recommendation in online karaokes. In Proceedings of the 7th ACM conference on Recommender systems (RecSys ''13). ACM, New York, NY, USA, 137-140.
[8] Yehuda Koren. 2009. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ''09). ACM, New York, NY, USA, 447-456.
[9] Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat- Thalmann. 2013. Time-aware point-of-interest recommendation. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (SIGIR ''13). ACM, New York, NY, USA, 363-372.
[10] Steffen Rendle and Lars Schmidt-Thieme. 2010. Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the third ACM international conference on Web search and data mining (WSDM ''10). ACM, New York, NY, USA, 81-90.
[11] Defu Lian, Vincent W. Zheng, and Xing Xie. 2013. Collaborative filtering meets next check-in location prediction. In Proceedings of the 22nd International Conference on World Wide Web(WWW ''13 Companion). ACM, New York, NY, USA, 231-232.

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