Matching Users' Preference Under Target Revenue Constraints in Optimal Data Recommendation Systems

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preferences and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to system with various system requirements and data distributions.Therefore,the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.Comment: 36 pages, 6 figure

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

PERSONALIZED SERVICES AS EMPATHIC RESPONSES: THE ROLE OF INTIMACY

Personalization that uses information technology to tailor content and products/services to the preferences and tastes of individual customers has become a useful function for online marketing. Many techniques have been developed, and research on personalized services has increased substantially in recent years. Several theories have been proposed to explain the effect of positive consumer attitude toward personalized services such as reducing information overload and the Elaboration Likelihood Model. These theories are grounded on a rational perspective. As personalization can be treated as an empathic response to the service receivers, we cannot ignore the role of emotion in a relationship building process. In this paper, we propose the relationship building (or Guanxi in Chinese) perspective in investigating the effectiveness of personalization, which treats intimate experience resulting from personalized response as an important factor to affect the receivers’ attitude towards the personalized recommendation. We conducted a controlled laboratory experiment on personalized recommendation to examine the role of intimacy in affecting consumer attitudes. Our findings indicated that intimate experience does mediate the effect of personalized response on consumer attitudes toward the recommendation. The results and findings provide valuable information to practitioners and researchers