Raymond Y. K. Lau

Least Squares Generative Adversarial Networks

Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We evaluate LSGANs on LSUN and CIFAR-10 datasets and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. We also conduct two comparison experiments between LSGANs and regular GANs to illustrate the stability of LSGANs.

Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning

With the widespread applications of electronic learning (e-Learning) technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Nevertheless, it is quite difficult, if not totally impossible, for instructors to read through and analyze the online messages to predict the progress of their students on the fly. The main contribution of this paper is the illustration of a novel concept map generation mechanism which is underpinned by a fuzzy domain ontology extraction algorithm. The proposed mechanism can automatically construct concept maps based on the messages posted to online discussion forums. By browsing the concept maps, instructors can quickly identify the progress of their students and adjust the pedagogical sequence on the fly. Our initial experimental results reveal that the accuracy and the quality of the automatically generated concept maps are promising. Our research work opens the door to the development and application of intelligent software tools to enhance e-Learning.

Towards a web services and intelligent agents-based negotiation system for B2B eCommerce

With the explosive growth of the number of transactions conducted via electronic channels, there is a pressing need for the development of intelligent support tools to improve the degree and sophistication of automation for eCommerce. With reference to the BBT business model, negotiation is one of key steps for B2B eCommerce. Nevertheless, classical negotiation models are ineffective for supporting multi-agent multi-issue negotiations often encountered in eBusiness environment. The first contribution of this paper is the exploitation of Web services and intelligent agent techniques for the design and development of a distributed service discovery and negotiation system to streamline B2B eCommerce. In addition, an effective and efficient integrative negotiation mechanism is developed to conduct multi-party multi-issue negotiations for B2B eCommerce. Finally, an empirical study is conducted to evaluate our intelligent agents-based negotiation mechanism and to compare the negotiation performance of our software agents with that of their human counterparts. Our research work opens the door to the development of the next generation of intelligent system solutions to support B2B eCommerce.

Text mining and probabilistic language modeling for online review spam detection

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

An evolutionary learning approach for adaptive negotiation agents

Developing effective and efficient negotiation mechanisms for real‐world applications such as e‐business is challenging because negotiations in such a context are characterized by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This article illustrates our adaptive negotiation agents, which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA‐based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism that guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real‐world applications.

Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix

Big data analytics have been embraced as a disruptive technology that will reshape business intelligence, which is a domain that relies on data analytics to gain business insights for better decision-making. Rooted in the recent literature, we investigate the landscape of big data analytics through the lens of a marketing mix framework in this paper. We identify the data sources, methods, and applications related to five important marketing perspectives, namely people, product, place, price, and promotion, that lay the foundation for marketing intelligence. We then discuss several challenging research issues and future directions of research in big data analytics and marketing related business intelligence in general.

Towards Genetically Optimised Multi-Agent Multi-Issue Negotiations

Classical negotiation models are based on a centralised decision making approach which assumes the availability of complete information about negotiators and unlimited computational resources. These negotiation mechanisms are ineffective for supporting real-world negotiations. This paper illustrates an agent-based distributive negotiation mechanism where each agents decision making model is independent to each other and is underpinned by an effective evolutionary learning algorithm to deal with complex and dynamic negotiation environments. Initial experimental results show that the proposed genetic algorithm (GA) based adaptive negotiation mechanism outperforms a theoretically optimal negotiation mechanism in environments constrained by limited computational resources and tough deadlines. Our research work opens the door to the development of practical negotiation systems for real-world applications.

Toward a semantic granularity model for domain-specific information retrieval

Both similarity-based and popularity-based document ranking functions have been successfully applied to information retrieval (IR) in general. However, the dimension of semantic granularity also should be considered for effective retrieval. In this article, we propose a semantic granularity-based IR model that takes into account the three dimensions, namely similarity, popularity, and semantic granularity, to improve domain-specific search. In particular, a concept-based computational model is developed to estimate the semantic granularity of documents with reference to a domain ontology. Semantic granularity refers to the levels of semantic detail carried by an information item. The results of our benchmark experiments confirm that the proposed semantic granularity based IR model performs significantly better than the similarity-based baseline in both a bio-medical and an agricultural domain. In addition, a series of user-oriented studies reveal that the proposed document ranking functions resemble the implicit ranking functions exercised by humans. The perceived relevance of the documents delivered by the granularity-based IR system is significantly higher than that produced by a popular search engine for a number of domain-specific search tasks. To the best of our knowledge, this is the first study regarding the application of semantic granularity to enhance domain-specific IR.

Combining social network and semantic concept analysis for personalized academic researcher recommendation

The rapid proliferation of information technologies especially Web 2.0 techniques has changed the fundamental ways how things can be done in many areas, including how researchers could communicate and collaborate with each other. The presence of the sheer volume of researchers and research information on the Web has led to the problem of information overload. There is a pressing need to develop researcher recommendation agents such that users can be provided with personalized recommendations of the researchers they can potentially collaborate with for mutual research benefits. In academic contexts, recommending suitable research partners to researchers can facilitate knowledge discovery and exchange, and ultimately improve the research productivity of researchers. Existing expertise recommendation research usually investigates the expert recommending problem from two independent dimensions, namely, their social relations and expertise information. The main contribution of this paper is that we propose a network based researcher recommendation approach which combines social network analysis and semantic concept analysis in a unified framework to improve the effectiveness of personalized researcher recommendation. The results of our experiment show that the proposed approach significantly outperforms the other baseline methods. Moreover, how our proposed framework can be applied to the real-world academic contexts is explained based on a case study.

Utilizing Search Intent in Topic Ontology-Based User Profile for Web Mining

It is well known that taking the Web user profiles into account can enhance the effectiveness of Web mining systems. However, due to the dynamic and complex nature of Web users, automatically acquiring worthwhile user profiles was found to be very challenging. Ontology-based user profile can possess more accurate user information. This research emphasizes on acquiring search intentions information. This paper presents a new approach of developing user profile for Web searching. The model considers the users search intentions by the process of PTM (Pattern-Taxonomy Model). Initial experiments show that the user profile based on search intention is more useful than the generic PTM user profile. Developing user profile that contains user search intentions is essential for effective Web search and retrieval