Hu Ke. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 56-58). / Abstracts in English and Chinese.

Qin An. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (p. 135-142). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

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

Contextual information retrieval (CIR) is a critical technique for today’s search engines in terms of facilitating queries and returning relevant information. Despite its importance, little progress has been made in its application, due to the difficulty of capturing and representing contextual information about users. This thesis details the development and evaluation of the contextual SERL search, designed to tackle some of the challenges associated with CIR from the World Wide Web. The contextual SERL search utilises a rich contextual model that exploits implicit and explicit data to modify queries to more accurately reflect the user’s interests as well as to continually build the user’s contextual profile and a shared contextual knowledge base. These profiles are used to filter results from a standard search engine to improve the relevance of the pages displayed to the user. The contextual SERL search has been tested in an observational study that has captured both qualitative and quantitative data about the ability of the framework to improve the user’s web search experience. A total of 30 subjects, with different levels of search experience, participated in the observational study experiment. The results demonstrate that when the contextual profile and the shared contextual knowledge base are used, the contextual SERL search improves search effectiveness, efficiency and subjective satisfaction. The effectiveness improves as subjects have actually entered fewer queries to reach the target information in comparison to the contemporary search engine. In the case of a particularly complex search task, the efficiency improves as subjects have browsed fewer hits, visited fewer URLs, made fewer clicks and have taken less time to reach the target information when compared to the contemporary search engine. Finally, subjects have expressed a higher degree of satisfaction on the quality of contextual support when using the shared contextual knowledge base in comparison to using their contextual profile. These results suggest that integration of a user’s contextual factors and information seeking behaviours are very important for successful development of the CIR framework. It is believed that this framework and other similar projects will help provide the basis for the next generation of contextual information retrieval from the Web.

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2006. / Includes bibliographical references (leaves: 77-86). Also available online.

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, Cape Town, 2005. / Includes bibliographical references (p. 132-142). Also available online.

With rapid growth of information on the Web, Web search engines have gained great momentum for exploiting valuable Web resources. Although keywords-based Web search engines provide relevant search results in response to users’ queries, future enhancement is still needed. Three important issues include (1) search results can be diverse because ambiguous keywords in queries can be interpreted to different meanings; (2) indentifying keywords in long queries is difficult for search engines; and (3) generating query-specific Web page summaries is desirable for Web search results’ previews. Based on clickthrough data, this thesis proposes a query-concept bipartite graph for representing queries’ relations, and applies the queries’ relations to applications such as (1) personalized query suggestions, (2) long queries Web searches and (3) query-specific Web page summarization. Experimental results show that query-concept bipartite graphs are useful for performance improvement for the three applications.

This paper examines the effects of serial position, price promotion, user experience and brand familiarity on search advertising in China. Past research on traditional media has hypothesized that TV ads in prime time and print ads in cover pages received more audience's attention than other ads placed in nonprime time spots on TV or other ads placed in inside pages in print media. Recent study finds the "banner blindness" phenomenon in interactive advertising due to user expertness. Past research also indicates that price promotion and brand familiarity have positive effects on consumer behavior. Will these theories also apply to the new media search engine? This study investigates whether higher ranked ads will result in higher click-through rates, whether "banner blindness" phenomenon also exists in search advertising and whether price promotion and brand familiarity lead to higher level of user attention and thus higher click-through rate. First, this paper analyzes advertisements in varied positions within the same context to better understand the effect of advertisement position ranking on consumer behavior, its role in advertising effectiveness, and the implications for interactive advertising and marketing communication. Second, it compares advertisement with price promotion message in ad copy with advertisement without price promotion message at the same ranking position within the same context. Finally, this study explores the relationship between user experience, brand familiarity and click-through rate. Major findings of this study include the following: first, primacy effect, price promotion and brand familiarity can lead to higher level of user attention to search advertisements and thus result in higher click-through rates. Second, user experience has a negative effect on search advertising effectiveness. The more experienced the users are, the less likely they click on search advertisements. Lastly, recency effect is not obtained in search advertising in this study. This study helps us better understand the effects of ad serial position, price promotion, user experience & brand familiarity on search advertising. It adds to our knowledge in search advertising and provides theoretical & practical implications for future research. / text

Using the proper search engine is crucial for efficient and effective web search. The objective of this thesis is to develop methodologies to evaluate search engines in a systematic and reliable manner. A new model for evaluation and comparison of search engines is proposed. This hierarchical model classifies the most common features found in search engines and search results into groups and subgroups. To illustrate the usefulness of the proposed model, several Chinese search engines are evaluated and compared as a case study. It is also very important to evaluate the performance of a search engine over time. Three performance measurement metrics are formulated for this purpose. Performance results for English and Chinese search engines are represented by histograms for visual inspection. The histograms are classified into groups to facilitate the interpretation of the performance metrics and examination of the associated behaviours of the search engines. An automated classification method is developed that eliminates the subjectivity and ambiguity found in visual classification of the histograms.

Contextual information retrieval (CIR) is a critical technique for today’s search engines in terms of facilitating queries and returning relevant information. Despite its importance, little progress has been made in its application, due to the difficulty of capturing and representing contextual information about users. This thesis details the development and evaluation of the contextual SERL search, designed to tackle some of the challenges associated with CIR from the World Wide Web. The contextual SERL search utilises a rich contextual model that exploits implicit and explicit data to modify queries to more accurately reflect the user’s interests as well as to continually build the user’s contextual profile and a shared contextual knowledge base. These profiles are used to filter results from a standard search engine to improve the relevance of the pages displayed to the user. The contextual SERL search has been tested in an observational study that has captured both qualitative and quantitative data about the ability of the framework to improve the user’s web search experience. A total of 30 subjects, with different levels of search experience, participated in the observational study experiment. The results demonstrate that when the contextual profile and the shared contextual knowledge base are used, the contextual SERL search improves search effectiveness, efficiency and subjective satisfaction. The effectiveness improves as subjects have actually entered fewer queries to reach the target information in comparison to the contemporary search engine. In the case of a particularly complex search task, the efficiency improves as subjects have browsed fewer hits, visited fewer URLs, made fewer clicks and have taken less time to reach the target information when compared to the contemporary search engine. Finally, subjects have expressed a higher degree of satisfaction on the quality of contextual support when using the shared contextual knowledge base in comparison to using their contextual profile. These results suggest that integration of a user’s contextual factors and information seeking behaviours are very important for successful development of the CIR framework. It is believed that this framework and other similar projects will help provide the basis for the next generation of contextual information retrieval from the Web.