Nikolay Karpov Pavel Shashkin National Research University Higher School of Economics 5th Int. Workshop on News Recommendation and Analytics (INRA.

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Presentation transcript:

Nikolay Karpov Pavel Shashkin National Research University Higher School of Economics 5th Int. Workshop on News Recommendation and Analytics (INRA 2017) Leipzig, Germany

Agenda Motivation Methods overview Dataset Model Results Conclusion Future plans

Motivation Our website have only implicit feedback State of the art implicit models (BRP, WARP) don't use time Time is important for news domain There are some other methods incorporate time (TimeSVD++) Our model inspired by TimeSVD++ No public dataset for evaluation

Agenda Motivation Methods overview Dataset Model Results Conclusion Future plans

Methods for Implicit Feedback Bayesian Personalized Ranking (BPR) 1 Pairwise sampling, optimize AUC Weighted Approximate-Rank Pairwise (WARP) 2 Optimize top items in a rank list 1. S.Rendle, C.Freudenthaler and Z.Gantner 2009 2. J. Weston, H. Yee, and R. J. Weiss 2013

Matrix Factorisation * =

Agenda Motivation Methods overview Dataset Model Results Conclusion Future plans

Dataset 600-800 thousand unique users a day 200-300 new articles a day 2 - 3 millions of user interactions a day 86% view in a first 24 hours after publishing

Agenda Motivation Methods overview Dataset Model Results Conclusion Future plans

Model Ф_U – user vector Ф_T – vector of average interest of users in a certain period of time Ф_I – item vector Ψ – scalar which reflects the average popularity of item in a certain period of time

Algorithm

Agenda Motivation Methods overview Dataset Model Results Conclusion Future plans

Results on News Dataset

Results on MovieLens 10M

Agenda Motivation Methods overview Dataset Model Results Conclusion Future plans

Conclusion We have introduced a model for implicit feedback recommender system. To consider temporal dynamics present in news domain we successfully apply a heuristic to factorization model. To our factorization model, we implement WARP algorithm for loss and sampling procedure. We also implement AdaGrad learning algorithm with Hogwild parallelization to a learning procedure. This model was evaluated on our specific news dataset, which we made available

Agenda Motivation Methods overview Dataset Model Results Conclusion Future plans

Future Plans NTD.TV – Multilanguage portal with news, videos, and viral content TheEpochTimes.com - Multilanguage portal with news, stories, and viral content Model for videos prediction Virality prediction Additional features: user profile, item content Mixture model (news+viral+…)

Thank you for your attention! Nikolay Karpov Associate Professor of NRU HSE Nizhny Novgorod, Russia