Pai xu suan fa ji qi zai wang luo sou suo zhong de ying yong

Wang, Wei.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 106-122).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.viChapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Thesis Contributions --- p.5Chapter 1.3 --- Thesis Organization --- p.8Chapter 2 --- Background and Literature Review --- p.9Chapter 2.1 --- Label Ranking in Machine Learning --- p.11Chapter 2.1.1 --- Label Ranking --- p.11Chapter 2.1.2 --- Semi-Supervised Learning --- p.12Chapter 2.1.3 --- The Development of Label Ranking --- p.14Chapter 2.2 --- Question Retrieval in Community Question Answering --- p.16Chapter 2.2.1 --- Question Retrieval --- p.16Chapter 2.2.2 --- Basic Question Retrieval Models --- p.18Chapter 2.2.3 --- The Development of Question Retrieval Models --- p.21Chapter 2.3 --- Ranking through CTR by Building Click Models --- p.24Chapter 2.3.1 --- Click Model's Importance --- p.24Chapter 2.3.2 --- A Simple Example of Click Model --- p.25Chapter 2.3.3 --- The Development of Click Models --- p.27Chapter 3 --- Semi-Supervised Label Ranking --- p.30Chapter 3.1 --- Motivation: The Limitations of Supervised Label Ranking --- p.30Chapter 3.2 --- Label Ranking and Semi-Supervised Learning Framework --- p.32Chapter 3.2.1 --- Label Ranking and Semi-Supervised Learning Setup --- p.32Chapter 3.2.2 --- Information Gain Decision Tree for Label Ranking --- p.37Chapter 3.2.3 --- Instance Based Label Ranking --- p.39Chapter 3.2.4 --- Mallows Model Decision Tree for Label Ranking --- p.40Chapter 3.3 --- Experiments --- p.40Chapter 3.3.1 --- Dataset Description --- p.41Chapter 3.3.2 --- Experimental Results --- p.42Chapter 3.3.3 --- Discussion --- p.42Chapter 3.4 --- Summary --- p.44Chapter 4 --- An Application of Label Ranking --- p.45Chapter 4.1 --- Motivation: The Limitations of Traditional Question Retrieval --- p.45Chapter 4.2 --- Intention Detection Using Label Ranking --- p.47Chapter 4.2.1 --- Question Intention Detection --- p.48Chapter 4.2.2 --- Label Ranking Algorithms --- p.50Chapter 4.2.3 --- Some Other Learning Algorithms --- p.53Chapter 4.3 --- Improved Question Retrieval Using Label Ranking --- p.54Chapter 4.3.1 --- Question Retrieval Models --- p.55Chapter 4.3.2 --- Improved Question Retrieval Model --- p.55Chapter 4.4 --- Experimental Setup --- p.56Chapter 4.4.1 --- Experiment Objective --- p.56Chapter 4.4.2 --- Experiment Design --- p.56Chapter 4.4.3 --- DataSet Description --- p.57Chapter 4.4.4 --- Question Feature --- p.59Chapter 4.5 --- Experiment Result and Comments --- p.60Chapter 4.5.1 --- Question Classification --- p.60Chapter 4.5.2 --- Classification Enhanced Question Retrieval --- p.63Chapter 4.6 --- Summary --- p.69Chapter 5 --- Ranking by CTR in Click Models --- p.71Chapter 5.1 --- Motivation: The Relational Influence's Importance in Click Models --- p.71Chapter 5.2 --- Click Models in Sponsored Search --- p.75Chapter 5.2.1 --- A Brief Review on Click Models --- p.76Chapter 5.3 --- Collaborating Influence Identification from Data Analysis --- p.77Chapter 5.3.1 --- Quantity Analysis --- p.77Chapter 5.3.2 --- Psychology Interpretation --- p.82Chapter 5.3.3 --- Applications Being Influenced --- p.82Chapter 5.4 --- Incorporating Collaborating Influence into CCM . --- p.83Chapter 5.4.1 --- Dependency Analysis of CCM --- p.83Chapter 5.4.2 --- Extended CCM --- p.84Chapter 5.4.3 --- Algorithms --- p.85Chapter 5.5 --- Incorporating Collaborating Influence into TCM . --- p.87Chapter 5.5.1 --- TCM --- p.87Chapter 5.5.2 --- Extended TCM --- p.88Chapter 5.5.3 --- Algorithms --- p.88Chapter 5.6 --- Experiment --- p.90Chapter 5.6.1 --- Dataset Description --- p.90Chapter 5.6.2 --- Experimental Setup --- p.91Chapter 5.6.3 --- Evaluation Metrics --- p.91Chapter 5.6.4 --- Baselines --- p.92Chapter 5.6.5 --- Performance on RMS --- p.92Chapter 5.6.6 --- Performance on Click Perplexity --- p.93Chapter 5.6.7 --- Performance on Log-Likelihood --- p.93Chapter 5.6.8 --- Significance Discussion --- p.98Chapter 5.6.9 --- Sensitivity Analysis --- p.98Chapter 5.7 --- Summary --- p.102Chapter 6 --- Conclusion and Future Work --- p.103Chapter 6.1 --- Conclusion --- p.103Chapter 6.2 --- Future Work --- p.105Bibliography --- p.10