MATEC Web of Conferences
Volume 22, 2015International Conference on Engineering Technology and Application (ICETA 2015)
|Number of page(s)||6|
|Section||Information and Communication Technology|
|Published online||09 July 2015|
User Adoption Tendency Modeling for Social Contextual Recommendation
School of Information Science and Technology, Sun Yat-sen University, Guangzhou, Guangdong, China
* Corresponding author: firstname.lastname@example.org
Most of studies on the existing recommender system for Netflix-style sites (scenarios with explicit user feedback) focus on rating prediction, but few have systematically analyzed users’ motivations to make decisions on which items to rate. In this paper, the authors study the difficult and challenging task Item Adoption Prediction (IAP) for predicting the items users will rate or interact with. It is not only an important supplement to previous works, but also a more realistic requirement of recommendation in this scenario. To recommend the items with high Adoption Tendency, the authors develop a unified model UATM based on the findings of Marketing and Consumer Behavior. The novelty of the model in this paper includes: First, the authors propose a more creative and effective optimization method to tackle One-Class Problem where only the positive feedback is available; second, the authors systematically and conveniently integrate the user adoption information (both explicit and implicit feedbacks included) and the social contextual information with quantitatively characterizing different users’ personal sensitivity to various social contextual influences.
Key words: recommender system / context-awareness / collaborative ranking / behavior modeling
© Owned by the authors, published by EDP Sciences, 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.