Enhancing Session-Based Recommendations through Sequential Modeling

Recommender systems typically determine the items they should recommend by learning models of user-preferences. Most often, those preferences are modeled as static and independent of context. In real life however, users consider items in sequence: TV series are watched episode by episode and accessories are chosen after the main appliance. Unfortunately, since sequences are more complex to model, they are often not taken into account. We developed an efficient sequence-modeling approach based on Bayesian Variable-order Markov Models and combined it with an existing content-based system, the Ontology Filtering. We tested this approach through live evaluations on two e-commerce sites. It dramatically increased performance, more than doubling the CTR and strongly increasing recommendation-mediated sales. These tests also confirm that the technique works efficiently and reliably in a production setting.