Most of the existing recommendation engines do not take into consideration contextual information for suggesting interesting items to users. Features such as time, location, or weather, may affect the user preferences for a particular item. In this paper, we propose two different context-aware approaches for the movie recommendation task. The first is an hybrid recommender that assesses available contextual factors related to time in order to increase the performance of traditional CF approaches. The second approach aims at identifying users in a household that submitted a given rating. This latter approach is based on machine learning techniques, namely, neural networks and majority voting classifiers. The effectiveness of both the approaches has been experimentally validated using several evaluation metrics and a large dataset.
Biancalana, C., Gasparetti, F., Micarelli, A., Miola, A., Sansonetti, G. (2011). Context-aware Movie Recommendation based on Signal Processing and Machine Learning. In Proceeding CAMRa '11 Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation (pp.5-10) [10.1145/2096112.2096114].