Using Skipping for Sequence-Based Collaborative Filtering

Recommender systems filter resources for a given user by predicting the most pertinent resource given a specific context. This paper describes a new approach of generating suitable recommendations based on the active user´s navigation stream. The underlying hypothesis is that the resources order in the stream results from the intrinsic logic of the user´s behavior. The sequence based recommender we propose is inspired from language modeling and integrates skipping techniques. 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 sequences to compute recommendations enhances the accuracy. Moreover, we propose a skipping variant that provides a high accuracy while being less complex.