Large-scale Collaborative Filtering Algorithms

隨著電子商務的市場爆炸性地成長，給予各種不同的客戶個人化的建議逐漸成為重要的議題。協同過濾為一項可組織與分析客戶喜好，並給予適當建議的重要技術。在此論文中，我們針對大規模的協同過濾演算法進行研究，以在可接受的時間內處理大量的資料。我們以著名的奇異值分解法做為演算法的基礎，並提出一些改進的方式，亦針對後處理的方法進行討論。我們參與了Netflix Prize此一關於預測對電影之喜好的競賽，並且得到良好的結果。As the market of electronic commerce grows explosively, it is important to provide customized suggestions for various consumers. Collaborative filtering is an important technique which models and analyzes the preferences of customers, and gives suitable advices. In this thesis, we study large-scale collaborative filtering algorithms to process huge data sets in acceptable time. We use the well-known Singular Value Decomposition as the basis of our algorithms, and propose some improvements. We also discuss post-processing methods. We participate at the competition of Netflix Prize, a contest of predicting movie preferences, and achieve good results.中文摘要 .............................................. iiBSTRACT ............................................. iiiIST OF FIGURES ....................................... viIST OF TABLES ....................................... viiHAPTER I. Introduction ...................................... 1 1.1 Problem Definition ............................. 1 1.2 Evaluation Criteria ............................ 2 1.3 Related Work ................................... 3 1.4 Summary ........................................ 5 II. Singular Value Decomposition ..................... 7 2.1 Formulation .................................... 7 2.2 Optimization .................................. 12 2.3 Variants ...................................... 16 2.4 Compound SVD .................................. 19 III. Post-Processing ................................ 24 3.1 Residuals ..................................... 24 3.2 K-Nearest Neighbors ........................... 25 3.3 Regression .................................... 28 IV. Experiments ..................................... 32 4.1 Data Sets ..................................... 32 4.2 Experimental Results .......................... 33 V. Conclusions ...................................... 38 BIBLIOGRAPHY ........................................ 3