Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Utilizing Physical and Social Context to Improve Recommender Systems
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
LARS: A Location-Aware Recommender System
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Hi-index | 0.00 |
To improve the quality of the recommendation of the recommendation system, a distance-interest affective model is proposed to combine user location context on the preferences of user interests. Based on the model and user-based collaborative filtering algorithm, the location context aware collective filtering algorithm is designed. Firstly, measure the location-similarity between users through the user's location context information. Second, calculate the origin user-similarity from the user-item rating matrix. Then, gain the location-similarity as a weight of final user similarity, calculate the final similarity. Finally, recommendation is supplied by top-N recommendation. The simulation results were compared with the traditional algorithm to prove the precision and recall rate of the proposed algorithm is superior to traditional algorithms.