Learning user preference patterns for Top-N recommendations

In this paper, we observe that the user preference styles tend to change regularly following certain patterns. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation- Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N recommendation in terms of accuracy. © 2012 IEEE.

Single-preference consensus measures based on models of ecological evenness

We investigate the relationship between consensus measures used in different settings depending on how voters or experts express their preferences. We propose some new models for single-preference voting, which we derive from the evenness concept in ecology, and show that some of these can be placed within the framework of existing consensus measures using the discrete distance. Finally, we suggest some generalizations of the single-preference consensus measures allowing the incorporation of more general notions of distance.

Library spaces designed with students in mind: an evaluation study of University of Queensland libraries at St Lucia campus

This paper identifies the design qualities of library spaces that matter the most foruniversity students. Drawing upon the data from an online survey made available to students from the University of Queensland, Australia, a number of design-related considerations are examined including: acoustics, furniture, interior architecture, lighting, and thermal comfort. 1505 students completed the survey, which aimed to assess how effective and responsive library spaces are in meeting students’ needs and supporting their learning experiences. The survey included ‘Likert scale questions’ requiring students to rate their levels of satisfaction with different aspects of library spaces and ‘open-ended questions’ asking students to elucidate their ratings. Findings revealed that the qualities of physical spaces were ranked as the third mostsignificant category of reasons accounting for students’ preference for certain library buildings over others, and for their frequency of visit (behind “location” of the library building and then “access to books and course-related materials or resources”). Design-related themes which emerged from qualitative analysis highlighted students’ awareness of the impacts that the design of spaces and furniture can have on their learning experiences. The study concludes with recommendations informed by students’ expectations, needs and preferences in relation to the qualities and features of library spaces.

A new fuzzy ranking method using fuzzy preference relations

In this paper, a new fuzzy ranking method for both type-1 and interval type-2 fuzzy sets (FSs) using fuzzy preference relations is proposed. The use of fuzzy preference relations to rank FSs with vertices has been introduced, and successfully implemented to undertake fuzzy multiple criteria hierarchical group decision-making problems. The proposed fuzzy ranking method is an extension of the results published in [1], and it is able to rank FSs with and without vertices. Besides that, it is important for a fuzzy ranking method to satisfy six reasonable fuzzy ordering properties as discussed in [6]-[8]. As a result, the capability of the proposed fuzzy ranking method in fulfilling these properties is analyzed and discussed. Issues related to time complexity of the proposed method are also examined.

Preference Networks: probabilistic models for recommendation systems

Abstract
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional
Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-N recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN.

Preference mining techniques for customer behavior analysis

The thesis has studied a number of critical problems in data mining for customer behavior analysis and has proposed novel techniques for better modeling of the customers’ decision making process, more efficient analysis of their travel behavior, and more effective identification of their emerging preference.

Motivational interviewing to explore culturally and linguistically diverse people's comorbidity medication self-efficacy

Aims and objectives: To examine the perceptions of a group of culturally and linguistically diverse participants with the comorbidities of diabetes, chronic kidney disease and cardiovascular disease to determine factors that influence their medication self-efficacy through the use of motivational interviewing. Background: These comorbidities are a global public health problem and their self-management is more difficult for culturally and linguistically diverse populations living in English-speaking communities. Few interventions have been tested in culturally and linguistically diverse people to improve their medication self-efficacy. Design: A series of motivational interviewing telephone calls were conducted in the intervention arm of a randomised controlled trial using interpreter services. Methods: Patients with these comorbidities aged ≥18 years of age whose preference it was to speak Greek, Italian or Vietnamese were recruited from nephrology outpatient clinics of two Australian metropolitan hospitals in 2009. Results: The average age of the 26 participants was 73·5 years. The fortnightly calls averaged 9·5 minutes. Thematic analysis revealed three core themes which were attitudes towards medication, having to take medication and impediments to chronic illness medication self-efficacy. A lack of knowledge about medications impeded confidence necessary for optimal disease self-management. Participants had limited access to resources to help them understand their medications. Conclusion: This work has highlighted communication gaps and barriers affecting medication self-efficacy in this group. Culturally sensitive interventions are required to ensure people of culturally and linguistically diverse backgrounds have the appropriate skills to self-manage their complex medical conditions. Relevance to clinical practice: Helping people to take their medications as prescribed is a key role for nurses to serve and protect the well-being of our increasingly multicultural communities. The use of interpreters in motivational interviewing requires careful planning and adequate resources for optimal outcomes.