Recommender systems (RS) have become increasingly popular in many web applications for eliminating online information overload and making personalized suggestions to users. In recent years, user personality has been recognized as valuable info to build more personalized recommender systems. However, the existing personality-based recommender systems has mainly focused on revealing the impact of personality on the user's preference over a single item or an attribute, which may ignore the impact of personality on users' perceptions of recommender systems when multiple recommendations are returned at the same time. In addition, they have mostly relied on personality quiz to explicitly acquire users' personality, which unavoidably demands user efforts. From users' perspective, they may be unwilling to answer the quiz for the sake of saving efforts or protecting their privacy. The application of existing personality-based recommender systems will thus be limited in real life.;In this thesis, we aim at 1) incorporating personality into top-N (N > 1) recommendations, with emphases on personalizing recommendation diversity and improving the recommendation interface design, 2) deriving users' personality from their implicit behavior for augmenting the existing recommender systems.;Specifically, we first develop a generalized, dynamic diversity adjusting approach based on user personality with the goal of achieving personalized diversity tailored to individual users' intrinsic needs. In particular, personality is integrated into a greedy re-ranking process, by which we select the item that can best balance accuracy and personalized diversity at each step, and then produce the final recommendation list. In this approach, personality is both used to estimate each user's diversity preference and to alleviate the cold-start problem of collaborative filtering recommendations. The experimental results demonstrate that our personalized diversity-oriented approach significantly outperforms related methods (including both non-diversity-oriented and diversity-oriented methods) in terms of both accuracy and diversity metrics, especially in the cold-start setting.;In addition to the algorithm development, designing diversity-oriented interface has been proven helpful to augment users' perception of recommendation diversity. However, little work has been done to identify the impact of users' personality on their preference for different types of recommendation interfaces (e.g., the diversity-oriented interface and the non-diversity-oriented interface). In order to fill the gap, we conduct a within-subject user study. We concretely compare a diversity-oriented organization-based recommendation interface with the standard ranked list interface covering three product domains with different investment levels and users' purchase experiences (i.e., mobile phone, hotel and movie). We find that users' perceptions of different recommendation interface are influenced by the product types. More notably, we identify the important role of users' personality in influencing their preference for recommendation interfaces. For instance, introverted users tend to reuse the organization-based interface in the future than the standard ranked list. The results can hence be constructive for improving existing recommendation interface design by considering users' personality.;Although personality has been proven effective at enhancing the multiple recommendations, the effort of explicitly acquiring users' personality traits via psychological questionnaire is unavoidably high, which may impede the application of personality-based recommenders in real life. We hence propose a generalized method to derive users' personality from their implicit behavior and further improve the existing recommender systems. A preliminary experiment has been conducted in movie domain. More specifically, we first identify a set of behavioral features through experimental validation, and develop inference model based on Gaussian Process to unify these features for determining users' big-five personality traits. We then test the model in a collaborative filtering based recommending framework on two real-life movie datasets, which demonstrates that our implicit personality based recommending algorithm significantly outperforms related methods in terms of both rating prediction and ranking accuracy. The experimental results point out an effective solution to boost the applicability of personality-based recommender systems in online environment.

M.Sc. (Information Technology) / Recommender systems assist a system user to identify relevant content within a specific context. This is typically performed through an analysis of a system user’s rating habits and personal preferences and leveraging these to return one or a number of relevant recommendations. There are numerable contexts in which recommender systems can be applied, such as movies, tourism, books, and music. The need for recommender systems has become increasingly relevant, particularly on the Internet. This is mainly due to the exponential amount of content that is published online on a daily basis. It has thus become more time consuming and difficult to find pertinent information online, leading to information overload. The relevance of a recommender system, therefore, is to assist a system user to overcome the information overload problem by identifying pertinent information on their behalf. There has been much research done within the recommender system field and how such systems can best recommend items to an individual user. However, a growing and more recent research area is how recommender systems can be extended to recommend items to groups, known as group recommendation. The relevance of group recommendation is that many contexts of recommendation apply to both individuals and groups. For example, people often watch movies or visit tourist attractions as part of a group. Group recommendation is an inherently more complex form of recommendation than individual recommendation for a number of reasons. The first reason is that the rating habits and personal preferences of each system user within the group need to be considered. Additionally, these rating habits and personal preferences can be quite heterogeneous in nature. Therefore, group recommendation becomes complex because a satisfactory recommendation needs to be one which meets the preferences of each group member and not just a single group member. The second reason why group recommendation is considered to be more complex than individual recommendation is because a group not only includes multiple personal preferences, but also multiple personality types. This means that a group is more complex from a social perspective. Therefore, a satisfactory group recommendation needs to be one which considers the varying personality types and behaviours of the group. The purpose of this research is to present PerTrust, a generic framework for group recommendation with the purpose of providing a possible solution to the aforementioned issues noted above. The primary focus of PerTrust is how to leverage both personality and trust in overcoming these issues.

In 1972, the passing of the British Library Act formally brought to an end an institutional relationship between the British Museum and the British Museum Library, which had lasted for over two hundred years. Since its creation in 1753, the Library had, in its capacity as the national deposit, developed a range of services and an infrastructure, which centred on the acquisition, storage and preservation of material for the national collection. However, in addition to meeting its legislative responsibilities, the Library had, from the very beginning, made itself increasingly responsible for the organisation and provision of the national collection for a growing academic usership. This desire, to fulfill both the function of a secure repository and of an educational resource, had throughout its history, provided the Library with the majority of the practical challenges that it faced in its day-to-day operations. However, between 1972-2000, the internal policy documents of the national library, now renamed the British Library, indicate a period of significant change, in which this study asserts a radical reorganisation of the Library's services and infrastructure was taking place. This thesis sets out by asking what evidence there is to support the assertion of a radical reorganisation of the national library during this period. The reformation of the national library as an autonomous institution in 1972, and the lead up to its subsequent relocation in 1997, naturally enough serve as starting points for this enquiry, which goes on to examine the discursive practices and theoretical issues that accompanied the formation of the new British Library. The changes noted in this study therefore, chart not only the transition from analogue to digital library services, but also the increasing relevance of the central discourses of librarianship - the provision, storage and classification of information - to information science as a whole.

By now, people are accustomed to getting some personalized recommendations when they are finding movies to watch, music to listen, and so on. All of these recommendations come from recommender systems, and can aid the process of the decision making to avoid the problem of "information overload". Over the years, there has been much work done both in industry and academia on developing new approaches for recommender systems. However, there are still some hurdles in adapting recommender systems to a broader range of real-life applications. In the e-commerce environment especially with the so called high-risk products (also called high-cost or high-involvement products, such as digital cameras, computers, and cars), because a user does not buy the high-risk product very often, it is normal that s/he is not able to rate many products. For the same reason, the current buyer is often a new user because s/he would not afford to buy the same kind of high-risk product before. The traditional recommender techniques (such as user-based collaborative filtering and content-based methods) can thus not be effectively applicable in this environment, because they largely assume that the users have prior experiences with products. Thus, the "data sparsity" and "new users" are two typical challenging issues that the classical recommender systems cannot well address in high-risk product domains. In some recommender systems, a new user will be asked to indicate his/her preferences on some aspects in order to address the so called cold-start problem via collecting some preferences. Such collected preferences are usually not complete due to unfamilaring with the product domain, which are called partial preferences.;In this thesis, we propose to leverage some auxiliary data of online reviewers' opinions, so as to enrich the partial preferences. With the objective of developing more effective recommender systems for high-risk products in e-commerce, in our work, we have exerted to derive reviewers' preferences from the textual reviews they posted. Then, these recovered preferences are leveraged to estimate and supplement a new buyer's preference with which the product recommendation is produced. Firstly, we propose a novel clustering method based on Latent Class Regression model (LCRM), which is able to consider both the overall ratings and feature-level opinion values (as extracted from textual reviews) to infer individual reviewers' weight feature preferences, that represent the weights the user places on different product features. Secondly, we propose a method to estimate reviewers' value preferences (i.e., the user's preferences on the product's attribute values) by matching their review opinions to the corresponding attributes' static specifications. Thirdly, we investigate how to combine weight preferences and value preferences to model user preferences based on Multi-Attribute Utility Theory (MAUT) with the purpose of providing higher quality product recommendations. Particularly, it was shown from our experimental studies that the incorporation of review information can significantly enhance the recommendation accuracy, relative to those without considering reviews. As the practical implication, our proposed solutions can be usefully plugged into an online system to be adopted in real-ecommerce sites.

Wang, Dingyan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 97-110). / 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 --- Motivations --- p.6 / Chapter 1.3 --- Contributions --- p.9 / Chapter 1.4 --- Organizations of This Thesis --- p.11 / Chapter 2 --- Background --- p.14 / Chapter 2.1 --- Label Propagation Learning Framework --- p.14 / Chapter 2.1.1 --- Graph-based Semi-supervised Learning --- p.14 / Chapter 2.1.2 --- Green's Function Learning Framework --- p.16 / Chapter 2.2 --- Recommendation Methods --- p.19 / Chapter 2.2.1 --- Traditional Memory-based Methods --- p.19 / Chapter 2.2.2 --- Traditional Model-based Methods --- p.20 / Chapter 2.2.3 --- Label Propagation Recommendation Models --- p.22 / Chapter 2.2.4 --- Latent Feature Recommendation Models . --- p.24 / Chapter 2.2.5 --- Social Recommendation Models --- p.25 / Chapter 2.2.6 --- Tag-based Recommendation Models --- p.25 / Chapter 3 --- Recommendation with Latent Features --- p.28 / Chapter 3.1 --- Motivation and Contributions --- p.28 / Chapter 3.2 --- Item Graph --- p.30 / Chapter 3.2.1 --- Item Graph Definition --- p.30 / Chapter 3.2.2 --- Item Graph Construction --- p.31 / Chapter 3.3 --- Label Propagation Recommendation Model with Latent Features --- p.33 / Chapter 3.3.1 --- Latent Feature Analysis --- p.33 / Chapter 3.3.2 --- Probabilistic Matrix Factorization --- p.35 / Chapter 3.3.3 --- Similarity Consistency Between Global and Local Views (SCGL) --- p.39 / Chapter 3.3.4 --- Item-based Green's Function Recommendation Based on SCGL --- p.41 / Chapter 3.4 --- Experiments --- p.41 / Chapter 3.4.1 --- Dataset --- p.43 / Chapter 3.4.2 --- Baseline Methods --- p.43 / Chapter 3.4.3 --- Metrics --- p.45 / Chapter 3.4.4 --- Experimental Procedure --- p.45 / Chapter 3.4.5 --- Impact of Weight Parameter u --- p.46 / Chapter 3.4.6 --- Performance Comparison --- p.48 / Chapter 3.5 --- Summary --- p.50 / Chapter 4 --- Recommendation with Social Network --- p.51 / Chapter 4.1 --- Limitation and Contributions --- p.51 / Chapter 4.2 --- A Social Recommendation Framework --- p.55 / Chapter 4.2.1 --- Social Network --- p.55 / Chapter 4.2.2 --- User Graph --- p.57 / Chapter 4.2.3 --- Social-User Graph --- p.59 / Chapter 4.3 --- Experimental Analysis --- p.60 / Chapter 4.3.1 --- Dataset --- p.61 / Chapter 4.3.2 --- Metrics --- p.63 / Chapter 4.3.3 --- Experiment Setting --- p.64 / Chapter 4.3.4 --- Impact of Control Parameter u --- p.65 / Chapter 4.3.5 --- Performance Comparison --- p.67 / Chapter 4.4 --- Summary --- p.69 / Chapter 5 --- Recommendation with Tags --- p.71 / Chapter 5.1 --- Limitation and Contributions --- p.71 / Chapter 5.2 --- Tag-Based User Modeling --- p.75 / Chapter 5.2.1 --- Tag Preference --- p.75 / Chapter 5.2.2 --- Tag Relevance --- p.78 / Chapter 5.2.3 --- User Interest Similarity --- p.80 / Chapter 5.3 --- Tag-Based Label Propagation Recommendation --- p.83 / Chapter 5.4 --- Experimental Analysis --- p.84 / Chapter 5.4.1 --- Douban Dataset --- p.85 / Chapter 5.4.2 --- Experiment Setting --- p.86 / Chapter 5.4.3 --- Metrics --- p.87 / Chapter 5.4.4 --- Impact of Tag and Rating --- p.88 / Chapter 5.4.5 --- Performance Comparison --- p.90 / Chapter 5.5 --- Summary --- p.92 / Chapter 6 --- Conclusions and Future Work --- p.94 / Chapter 6.0.1 --- Conclusions --- p.94 / Chapter 6.0.2 --- Future Work --- p.96 / Bibliography --- p.97

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 90-91).

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, December 2003. / Thesis advisor(s): Nita L. Miller, Thomas J. Housel. Includes bibliographical references (p. 115-117). Also available online.

Thesis (M.S.W.)--University of Hong Kong, 1980. / Typescript.

Thesis (M. Sc.)--University of Hong Kong, 1978.

Thesis (M.B.A.)--University of Hong Kong, 1979.