A Low-Order Markov Model integrating Long-Distance Histories for Collaborative Recommender Systems

International audienceRecommender systems provide users with pertinent resources according their context and their profiles, by applying statistical and knowledge discovery techniques. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream, by considering long and short-distance resources in the history with a tractable model. The Skipping Based Recommender we propose uses Markov models inspired from the ones used in language modeling while integrating skipping techniques to handle noise during navigation. Weighting schemes are also used to alleviate the importance of distant resources. This recommender has also the characteristic to be anytime. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous resources to compute recommendations enhances the accuracy. Moreover, the skipping variant we propose provides a high accuracy while being less complex than state of the art variants