Abstract: Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.
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.