Collaborative filtering inspired from language modeling

Recommender systems filter resources for a given user by predicting the most pertinent item 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 items order in the stream results from the intrinsic logic of the user´s behavior. We show similarities between natural language and Internet navigation and put forward navigation specificities. We then design a new model that integrates advantages of statistical language models such as n-grams and triggers to compute recommendations. The resulting Sequence Based Recommender has been tested on Internet navigation artificial corpora.