Fab: content-based, collaborative recommendation
Communications of the ACM
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
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
Context-based splitting of item ratings in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Intelligent techniques for web personalization
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
An automatic classification system for consumer regulatory focus by analyzing web shopping logs
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Knowledge-Based Systems
A location-based news article recommendation with explicit localized semantic analysis
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 12.08 |
Recommender systems are the efficient and most used tools that prevail over the information overload problem, provide users with the most appropriate content by considering their personal preferences (mostly, ratings). In addition to these preferences, taking into account the interaction context of users will improve the relevancy of the recommendation process. However, only a few prior studies have tried to adopt context-awareness to the recommendation model. Although a number of studies have developed recommendation models using collaborative filtering (CF), few of them have tried to adopt both CF and other artificial intelligence techniques, such as genetic algorithm (GA), as a tool to improve recommendation results. In this paper, we propose a new recommendation model, which we termed Context-Aware Collaborative Filtering using genetic algorithm (CACF-GA), for location-based advertising (LBA) based on both user's preferences and interaction's context. We first defined discrete contexts, and then applied the concept of ''context similarity'' to conventional CF to create the context-aware recommendation model. The context similarity between two contexts is designed to be optimized using GA. We collect real-world data from mobile users, build a LBA recommendation model using CACF-GA, and then perform an empirical test to validate the usefulness of CACF-GA. Experiments show our proposed model provides the most accurate prediction results compared to comparative ones.