Abstract
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.
A. Burashnikova is supported by the Analytical center under the RF Government (subsidy agreement 000000D730321P5Q0002, Grant No. 70-2021-00145 02.11.2021). Y. Maximov is supported by LANL LDRD projects.
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The source code will be made available for research purpose.
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Burashnikova, A., Clausel, M., Amini, MR., Maximov, Y., Dante, N. (2022). Recommender Systems: When Memory Matters. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_7
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