Predicting Most Rated Items in Weekly Recommendation with Temporal Regression
This paper presents our approach to contextual recommendation for the Filmtipset Weekly Recommendation Track of the CAMRA 2010 Challenge. The goal of this task is to predict which items will be rated by each user on specific weeks of the year, namely the week containing Christmas day, and the week leading up to the Oscars, based on ratings collected prior to the test period. Our approach aims at modeling the short-term evolution of the probability that an item is rated before each test period (in a user-independent way), and then forecasting these probabilities on the test week. To that end, we use a temporal regression technique providing non-personalized recommendation with better test performances than other non-personalized recommendation baselines. We then tried, with success, to generate time-dependent collaborative personalized recommendations providing us our best results.
ACM SIGKDD Workshop on Context-Aware Recommender Systems 2011