Recommender Systems: When Memory Matters

Aleksandra Burashnikova, Marianne Clausel, Massih Reza Amini, Yury Maximov, Nicolas Dante

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper, we study the effect of non-stationarities and memory in the learnability of a sequential recommender system that exploits user’s implicit feedback. We propose an algorithm, where model parameters are updated user per user by minimizing a ranking loss over blocks of items constituted by a sequence of unclicked items followed by a clicked one. We illustrate through empirical evaluations on four large-scale benchmarks that removing non-stationarities, through an empirical estimation of the memory properties, in user’s behaviour interactions allows to gain in performance with respect to MAP and NDCG.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 44th European Conference on IR Research, ECIR 2022, Proceedings
EditorsMatthias Hagen, Suzan Verberne, Craig Macdonald, Christin Seifert, Krisztian Balog, Kjetil Nørvåg, Vinay Setty
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030997380
Publication statusPublished - 2022
Event44th European Conference on Information Retrieval, ECIR 2022 - Stavanger, Norway
Duration: 10 Apr 202214 Apr 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13186 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference44th European Conference on Information Retrieval, ECIR 2022


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