GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Charting past, present, and future research in ubiquitous computing
ACM Transactions on Computer-Human Interaction (TOCHI) - Special issue on human-computer interaction in the new millennium, Part 1
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Generalized Version Space Learning Algorithm for Noisy and Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
Link Contexts in Classifier-Guided Topical Crawlers
IEEE Transactions on Knowledge and Data Engineering
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Accounting for taste: using profile similarity to improve recommender systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings for ICPS: 2006 International Conference on Pervasive Services
PERSER '06 Proceedings of the 2006 ACS/IEEE International Conference on Pervasive Services
Ontology-based context synchronization for ad hoc social collaborations
Knowledge-Based Systems
Managing Context Information in Mobile Devices
IEEE Pervasive Computing
Contextualized Recommendation Based on Reality Mining From Mobile Subscribers
Cybernetics and Systems
On-line personalized sales promotion in electronic commerce
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
MAPIS, a multi-agent system for information personalization
Information and Software Technology
Expansion of telecommunication social networks
CDVE'07 Proceedings of the 4th international conference on Cooperative design, visualization, and engineering
Centrality measurement on semantically multiplex social networks: divide-and-conquer approach
International Journal of Intelligent Information and Database Systems
Information seeking in social context: structural influences andreceipt of information benefits
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Location-dependent services for mobile users
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
WTS'10 Proceedings of the 9th conference on Wireless telecommunications symposium
Building a targeted mobile advertising system for location-based services
Decision Support Systems
| Hi-index | 12.05 |
Personal context is the most significant information for providing contextualized mobile recommendation services at a certain time and place. However, it is very difficult for service providers to be aware of the personal contexts, because each person's activities and preferences are very ambiguous and depending on numerous unknown factors. In order to deal with this problem, we have focused on discovering social relationships (e.g., family, friends, colleagues and so on) between people. We have assumed that the personal context of a certain person is interrelated with those of other people, and investigated how to employ his neighbor's contexts, which possibly have a meaningful influence on his personal context. It indicates that we have to discover implicit social networks which express the contextual dependencies between people. Thereby, in this paper, we propose an interactive approach to build meaningful social networks by interacting with human experts. Given a certain social relation (e.g., isFatherOf), this proposed systems can evaluate a set of conditions (which are represented as propositional axioms) asserted from the human experts, and show them a social network resulted from data mining tools. More importantly, social network ontology has been exploited to consistently guide them by proving whether the conditions are logically verified, and to refine the discovered social networks. We expect these social network is applicable to generate context-based recommendation services. In this research project, we have applied the proposed system to discover the social networks between mobile users by collecting a dataset from about two millions of users.