Location-Based Services — An Overview of the Standards
BT Technology Journal
IEEE Transactions on Knowledge and Data Engineering
Recency-based collaborative filtering
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Adapting neighborhood and matrix factorization models for context aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Simple time-biased KNN-based recommendations
Proceedings of the Workshop on Context-Aware Movie Recommendation
New approaches to mood-based hybrid collaborative filtering
Proceedings of the Workshop on Context-Aware Movie Recommendation
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Adaptive Location-Oriented Content Delivery in Delay-Sensitive Pervasive Applications
IEEE Transactions on Mobile Computing
Improving Recommender Systems by Incorporating Social Contextual Information
ACM Transactions on Information Systems (TOIS)
Proceedings of the 20th international conference companion on World wide web
Proceedings of the 20th international conference on World wide web
Group recommendation in context
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
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Recently context-aware recommender systems have become one of the hottest topics in the domain of recommender systems. Household can be seen as an important context to user ratings, and therefore may play an important role in improving recommendation accuracy. In the paper, we propose a heuristic approach to identify which member of a household performs a specific rating by considering rating-related timestamp and household information. Firstly, we employ rating-related timestamp to select the appropriate user ratings in order to avoid introducing the outdated ratings, and then employ household information into the process of user rating prediction. Next, we design a heuristic approach to calculate the probabilities that a given item is rated by different members in a household based on the above prediction ratings. Finally, in order to enhance probability estimation accuracy, we analyze difference influences of different household members and introduce them into the final process of household member identification. We perform experimental comparisons of the above approach with some baselines on the Moviepilot dataset released for the Challenge on Context-Aware Movie Recommendation (CAMRa2011), and also analyze the results.