Survey of data mining approaches to user modeling for adaptive hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio

REVIEW AND ANALYSIS OF MACHINE LEARNING AND SOFT COMPUTING APPROACHES FOR USER MODELING

ABSTRAC

Towards building a review recommendation system that trains novices by leveraging the actions of experts

Online reviews increase consumer visits, increase the time spent on the website, and create a sense of community among the frequent shoppers. Because of the importance of online reviews, online retailers such as Amazon.com and eOpinions provide detailed guidelines for writing reviews. However, though these guidelines provide instructions on how to write reviews, reviewers are not provided instructions for writing product-specific reviews. As a result, poorly-written reviews are abound and a customer may need to scroll through a large number of reviews, which could be up to 6000 pixels down from the top of the page, in order to find helpful information about a product (Porter, 2010). Thus, there is a need to train reviewers to write better reviews, which could in turn better serve customers, vendors, and online e-stores. In this Thesis, we propose a review recommendation framework to train reviewers to better write about their experiences with a product by leveraging the behaviors of expert reviewers who are good at writing helpful reviews. First, we use clustering to model reviewers into different classes that reflect different skill levels to write a quality review such as expert, novice, etc. Through temporal analysis of reviewer behavior, we have found that reviewers evolve over time, with their reviews becoming better or worse in quality and more or less in quantity. We also investigate how reviews are valued differently across different product categories. Through machine learning-based classification techniques, we have found that, for products associated with prevention consumption goal, longer reviews are perceived to be more helpful; and, for products associated with promotion consumption goal, positive reviews are more helpful than negative ones. In this Thesis, our proposed review recommendation framework is aimed to help a novice or conscientious reviewer become an expert reviewer. Our assumption is that a reviewer will reach the highest level of expertise by learning from the experiences of his or her closest experts who have a similar evolutionary pattern to that of the reviewer who is being trained. In order to provide assistance with intermediate steps for the reviewer to grow from his or her current state to the highest level of expertise, we want to recommend the positive actions—that are not too far out of reach of the reviewer—and discourage the negative actions—that are within reach of the reviewer—of the reviewer’s closest experts. Recommendations are personalized to fit the expertise level of reviewers, their evolution trend, and product category. Using the proposed review recommendation system framework we have found that for a random reviewer, at least 80% of the reviews posted by closest experts were of higher quality than that of the novice reviewer. This is verified in a dataset of 2.3 million reviewers, whose reviews cover products from nine different product categories such as Books, Electronics, Cellphones and accessories, Grocery and gourmet food, Office product, Health and personal care, Baby, Beauty, and Pet supplies. Advisor: Leen-Kiat So

The development of an intelligent decision support framework in the contact centre environment

In a time of fast growing technology and communication systems, it is very important for the industry and the corporations to develop new contact centre environment technologies for better customer contact requirements. The integration of contact centre (CC) into day-to-day organisational operations represents one of the most promising trends in the 21 st century economy. Whatever the nature or point of contact, customers want a seamless interaction throughout their experience with the company. Customers receive more personalised experience, while the company itself can now provide a consistent message across all customer interactions. Based on the literature studies and the research carried out within the contact centre industry through the case studies, the author identified the customer and advisor behavioural attributes along with demographic, experience and others that later are used to derive the categories. Clustering technique identified the categories for customers and advisors. From the initial set of categories, fuzzy expert system framework was derived which assigned a customer or advisor with the pre-defined set of categories. The thesis has proposed two novel frameworks for categorisation of customer and advisor within contact centres and development of intelligent decision support framework that displays the right amount of information to the advisor at the right time. Furthermore, the frameworks were validated with qualitative expert judgement from the experts at the contact centres and through a simulation approach. The research has developed a novel Soft Computing based fuzzy logic categorisation framework that categorises customer and advisor on the basis of their demographic, experience and behavioural attributes. The study also identifies the behavioural aspects of customer and advisor within CC environment and on the basis of categorisation framework, assigns each customer and advisor to that of a pre-defined category. The research has also proposed an intelligent decision support framework to identify and display the minimum amount of information required by an advisor to serve the customer in CC environment. The performance of the proposed frameworks is analysed through four case studies. In this way this research proposes a fully tested and validated set of categorisation and information requirement frameworks for dealing with customer and advisor and its challenges. The research also identifies future research directions in the relevant areas.EThOS - Electronic Theses Online ServiceGBUnited Kingdo