Abstract: Knowledge modelling, a main activity for the development of Knowledge Based Systems, have no set standards and are mostly done in an ad hoc way. There is a lack of support for the transition from abstract level to implementation. In this paper, a methodology for the development of the knowledge model, which is inspired by both Software and Knowledge Engineering, is proposed. Use of UML which is the de-facto standard for modelling in the software engineering arena is explored for knowledge modelling. The methodology proposed, is used to develop a knowledge model of a knowledge based system for recommending suitable hotels for tourists visiting Mauritius.
Abstract: In this paper, we present an approach for soccer video
edition using a multimodal annotation. We propose to associate with
each video sequence of a soccer match a textual document to be used
for further exploitation like search, browsing and abstract edition.
The textual document contains video meta data, match meta data, and
match data. This document, generated automatically while the video
is analyzed, segmented and classified, can be enriched semi
automatically according to the user type and/or a specialized
recommendation system.
Abstract: Recommender systems are usually regarded as an
important marketing tool in the e-commerce. They use important
information about users to facilitate accurate recommendation. The
information includes user context such as location, time and interest
for personalization of mobile users. We can easily collect information
about location and time because mobile devices communicate with the
base station of the service provider. However, information about user
interest can-t be easily collected because user interest can not be
captured automatically without user-s approval process. User interest
usually represented as a need. In this study, we classify needs into two
types according to prior research. This study investigates the
usefulness of data mining techniques for classifying user need type for
recommendation systems. We employ several data mining techniques
including artificial neural networks, decision trees, case-based
reasoning, and multivariate discriminant analysis. Experimental
results show that CHAID algorithm outperforms other models for
classifying user need type. This study performs McNemar test to
examine the statistical significance of the differences of classification
results. The results of McNemar test also show that CHAID performs
better than the other models with statistical significance.
Abstract: This study examines the issue of recommendation
sources from the perspectives of gender and consumers- perceived
risk, and validates a model for the antecedents of consumer online
purchases. The method of obtaining quantitative data was that of the
instrument of a survey questionnaire. Data were collected via
questionnaires from 396 undergraduate students aged 18-24, and a
multiple regression analysis was conducted to identify causal
relationships. Empirical findings established the link between
recommendation sources (word-of-mouth, advertising, and
recommendation systems) and the likelihood of making online
purchases and demonstrated the role of gender and perceived risk as
moderators in this context. The results showed that the effects of
word-of-mouth on online purchase intentions were stronger than those
of advertising and recommendation systems. In addition, female
consumers have less experience with online purchases, so they may be
more likely than males to refer to recommendations during the
decision-making process. The findings of the study will help
marketers to address the recommendation factor which influences
consumers- intention to purchase and to improve firm performances to
meet consumer needs.