Predicting Most Rated Items in Weekly Recommendation with Temporal Regression

Abstract : This paper presents our approach to contextual recommendation for the Filmtipset Weekly Recommendation Track of the CAMRA 2010 Challenge[5]. 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.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01286116
Contributor : Lip6 Publications <>
Submitted on : Thursday, March 10, 2016 - 11:58:19 AM
Last modification on : Thursday, March 21, 2019 - 2:43:13 PM

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Bruno Pradel, Antoine Brenner, Nicolas Usunier, Patrick Gallinari. Predicting Most Rated Items in Weekly Recommendation with Temporal Regression. Workshop on Context-Aware Movie Recommendation, CAMRa '10, Sep 2010, Barcelona, Spain. pp.24-27, ⟨10.1145/1869652.1869656⟩. ⟨hal-01286116⟩

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