Towards personalized context-aware recommendation by mining context logs through topic models

Authors:
Kuifei Yu;Baoxian Zhang;Hengshu Zhu;Huanhuan Cao;Jilei Tian
Affiliations:
Graduate University of Chinese Academy of Sciences, China,Nokia Research Center, China;Graduate University of Chinese Academy of Sciences, China;Nokia Research Center, China,University of Science and Technology of China, China;Nokia Research Center, China;Nokia Research Center, China
Venue:
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2012

Citing 11
Cited 2

Probabilistic latent semantic indexing

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text Classification from Labeled and Unlabeled Documents using EM

Machine Learning - Special issue on information retrieval
Investigating the relationship between language model perplexity and IR precision-recall measures

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Latent dirichlet allocation

The Journal of Machine Learning Research
A Personalized Restaurant Recommender Agent for Mobile E-Service

EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications

ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering

Proceedings of the fourth ACM conference on Recommender systems
An Unsupervised Approach to Modeling Personalized Contexts of Mobile Users

ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Context-aware POI recommendations in an automotive scenario using multi-criteria decision making methods

Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
Place-Its: a study of location-based reminders on mobile phones

UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Location-based recommendation system using Bayesian user's preference model in mobile devices

UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing

Exploiting enriched contextual information for mobile app classification

Proceedings of the 21st ACM international conference on Information and knowledge management
Leveraging biosignal and collaborative filtering for context-aware recommendation

Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare

Quantified Score

Hi-index	0.00

Visualization

Abstract

The increasing popularity of smart mobile devices and their more and more powerful sensing ability make it possible to capture rich contextual information and personal context-aware preferences of mobile users by user context logs in devices. By leveraging such information, many context-aware services can be provided for mobile users such as personalized context-aware recommendation. However, to the best knowledge of ours, how to mine user context logs for personalized context-aware recommendation is still under-explored. A critical challenge of this problem is that individual user's historical context logs may be too few to mine their context-aware preferences. To this end, in this paper we propose to mine common context-aware preferences from many users' context logs through topic models and represent each user's personal context-aware preferences as a distribution of the mined common context-aware preferences. The experiments on a real-world data set contains 443 mobile users' historical context data and activity records clearly show the approach is effective and outperform baselines in terms of personalized context-aware recommendation.