HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Augmenting content-based rating prediction with link stream features

Abstract : While graph-based collaborative filtering recommender systems have been introduced several years ago, there are still several shortcomings to deal with, the temporal information being one of the most important. The new link stream paradigm is aiming at extending graphs for correctly modelling the graph dynamics, without losing crucial information. We investigate the impact of such link stream features for recommender systems. We design link stream features, that capture the intrinsic structure and dynamics of the data. We show that such features encode a fine-grained and subtle description of the underlying system. We focused on a traditional recommender system context, the rating prediction on the MovieLens20M movie dataset and the Goodreads book dataset. We input link stream features along with some content-based ones into a gradient boosting machine (XGBoost) and show that it outperforms significantly a sole content-based solution. These encouraging results call for further exploration of this original modelling and its integration to complete state-of-the-art recommender systems algorithms. Link streams and graphs, as natural visualizations of recom-mender systems, may offer more interpretability in a time when algorithm transparency is an increasingly important topic of discussion. We also hope that these results will sparkle interesting discussions in the community about the connections between link streams and traditional methods (matrix fac-torization, deep learning).
Complete list of metadata

Cited literature [30 references]  Display  Hide  Download

Contributor : Raphaël Fournier-S'Niehotta Connect in order to contact the contributor
Submitted on : Friday, January 17, 2020 - 4:32:55 PM
Last modification on : Friday, March 25, 2022 - 9:58:03 PM


Files produced by the author(s)




Tiphaine Viard, Raphaël Fournier-S'Niehotta. Augmenting content-based rating prediction with link stream features. Computer Networks, Elsevier, 2019, 150, pp.127-133. ⟨10.1016/j.comnet.2018.12.002⟩. ⟨hal-02444202⟩



Record views


Files downloads