An Adaptive Interface for Customer Transaction Assistant in Electronic Commerce

Personalized service and adaptive interface play important factors in electronic commerce. This work proposes an adaptive interface to for helping the customer transaction in electronic commerce. The adaptive interface collects the consumer behaviors by monitoring the customer operations, excluding unnecessary operations, and recognizing the behavior patterns. The interface uses the Bayesian belief network and the RBF neural networks to achieve the above tasks. The interface then evaluates knowledge and skill proficiency according to the customer behavior patterns. Finally, the interface generates the adaptive interface to the consumers for helping the transaction process

A Personalized Recommender System Based on Explanation Facilities Using Collaborative Filtering

Collaborative filtering (CF) is the most successful recommendation method, but its widespread use has exposed some limitations, such as sparsity, scalability, and black box. Many researchers have focused on sparsity and scalability problem but a little has tried to solve the black box problem. Most CF recommender systems are black boxes, providing no transparency into the working of the recommendation. This research suggests an improved CF recommender system with explanation facilities to overcome the black box problem. Explanation facilities make it possible to expose the reasoning and data behind a recommendation. Therefore, explanations provide us with a mechanism for handling errors that come with a recommendation. Furthermore, it is proposed to use web usage mining and product taxonomy to enhance the recommendation quality for e-commerce environment. For such purposes, it is developed a recommender system named WebCF-Exp, Web usage mining driven Collaborative Filtering with Explanation facilities. To test the performance of WebCF-Exp, EBIB research internet shopping mall and explanation interfaces are developed. Experiments are conducted with the data provided by EBIB Research Internet shopping mall

Supporting Undergraduate Research: Recommending Personalized Research Projects to Undergraduates

Undergraduates’ participation in faculty-mentored research is becoming an important issue in tertiary education in recent years, and it benefits both undergraduates and faculty members. In reality, many faculty members have research projects that need help from undergraduates, but undergraduates can hardly find the information, which creates information asymmetry problem. Besides, undergraduates lack the experience of doing academic research, the research interest information is incomplete, so they have difficulties in choosing suitable research projects. Thus recommender systems are necessary to facilitate undergraduates’ participation in research projects. Traditional recommendation approaches require relative complete information for decision making, and they can hardly meet the requirements as undergraduates’ research information is incomplete. In this study, we propose a two-stage model that integrates content-based method with collaborative method by leveraging research social networks, where undergraduates are encouraged to connect with faculty members and participate in social network activities, through which research information is collected. The proposed two-stage model alleviates the problems of information asymmetry and incomplete information. The recommender system has been developed in ScholarMate (www.scholarmate.com), and it allows undergraduates to choose suggested research projects

Combing Customer Profiles for Members' Repurchase Rate Predictions

[[abstract]]Customer relationship management (CRM) leverages historical users’ behaviors in order to dawn effort of enhancing customer satisfaction and loyalty. Thus, constructing a successful customer profile plays a critical role in CRM. As customers’ preferences may change over time, we take the different types of past behavior patterns of the registered members to capture concept drifts. Then, we combine the repurchase index (RI) and the preference drifts to propose a Behavioral Repurchase Prediction (BRP) model, and to predict the members’ repurchase rates in the specific category of the e-shop. The marketers of the e-shop can target the registered members with high repurchase rates and design corresponding marketing strategies. The experimental results with a real dataset show that our model can effectively predict the registered members’ repurchase rates.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal

The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented

Czy handel elektroniczny wydłuża czas życia produktu?

In the first part of article the theory of product lifecycle were presented. Then the main features of e-commerce which have the influence on lengthening the product lifecycle were analysed. In the next part of article the results of undertaken research about relationship between the age of products and their accessibility in internet and brick and mortal shops were shown. The last part of article presents the conclusions

Czy handel elektroniczny wydłuża czas życia produktu?

In the first part of article the theory of product lifecycle were presented. Then the main features of e-commerce which have the influence on lengthening the product lifecycle were analysed. In the next part of article the results of undertaken research about relationship between the age of products and their accessibility in internet and brick and mortal shops were shown. The last part of article presents the conclusions

Recomendación de objetos de aprendizaje con base en opiniones escritas por usuarios

Actualmente la cantidad de recursos educativos disponibles en Internet ha crecido hasta límites insospechados, dando lugar al problema de sobrecarga de información. La tarea de buscar recursos que sean relevantes para los usuarios se ha convertido en una tarea tediosa. Con el fin de facilitarles la tarea a los usuarios y presentarles solo los recursos que ellos necesitan se implementan los sistemas recomendadores en el dominio de e-learning. Los métodos actuales de recomendación necesitan que los usuarios valoren los objetos de aprendizaje, muchos usuarios son reacios a valorarlo de forma explícita y el éxito de las recomendaciones depende de la cantidad de valoraciones obtenidas. Es común que en lugar de completar formularios con valoraciones de dichos objetos, muchos usuarios prefieran usar el lenguaje natural y expresar sus opiniones sobre ellos en forma de texto libre, similar a una conversación con un amigo. En este artículo se presenta un mecanismo que formaliza el proceso de selección y la recuperación de las opiniones textuales sobre objetos de aprendizaje y la utilización de esas opiniones para la recomendación de recursos educativos. Los resultados obtenidos demuestran que los usuarios se sienten más satisfechos con recomendaciones soportadas con base en el juicio de otras personas