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July 6, 2026
Ancient Craniiform Brachiopod: A Newly Discovered Species with a Unique Shell Shape and Lifestyle
Scientists from HSE University, MSU, and Tallinn University of Technology have studied a fossil species of ancient brachiopods that lived in a warm sea in what is now northern Estonia more than 445 million years ago. These ancient brachiopods developed a cup-shaped shell with a protective 'cap' that shielded them from overgrowth by other marine organisms. The study has been published in Palaeogeography, Palaeoclimatology, Palaeoecology.
July 2, 2026
Researchers Discover How Spelling Errors Slow Down Reading in Russian
Psycholinguists from the Centre for Language and Brain at HSE University–St Petersburg have shown that words that are frequently misspelled are processed more slowly by readers, even when presented with the correct spelling. The researchers confirmed this effect for the first time using Russian-language materials and found that response speed is most strongly linked to how confidently individuals can distinguish the correct spelling of a word from an incorrect one. The study has been published in The Mental Lexicon.
July 2, 2026
HSE Develops App for Assessing Phonological Processing in Children
Researchers at the HSE Centre for Language and Brain have developed a new digital tool for assessing children's phonological processing skills—the ZARYA (Sound Analysis of the Russian Language) test battery. It is the first standardised application in Russia designed to provide a fast and reliable assessment of children's ability to distinguish speech sounds, retain them in working memory, and perform phonemic analysis. The app runs on Android tablets and smartphones and is available for download from RuStore. Details of the test validation have been published in the Journal of Speech, Language, and Hearing Research.

 

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RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Association for Computing Machinery (ACM), 2023.

ACM COPYRIGHT NOTICE. Copyright ©2023 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org. For other copying of articles that carry a code at the bottom of the first or last page, copying is permitted provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, USA.

Chapters
Time-Aware Item Weighting for the Next Basket Recommendations
Romanov A., Lashinin O., Ananyeva M. et al., , in: RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems.: Association for Computing Machinery (ACM), 2023. P. 985–992.
In this paper we study the next basket recommendation problem. Recent methods use different approaches to achieve better perfor- mance. However, many of them do not use information about the time of prediction and time intervals between baskets. To fill this gap, we propose a novel method, Time-Aware Item-based Weight- ing (TAIW), which takes timestamps ...
Added: September 27, 2023
Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?
Klenitskiy Anton, Alexey Vasilev, , in: RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems.: Association for Computing Machinery (ACM), 2023. P. 1120–1125.
Added: November 24, 2024
Ti-DC-GNN: Incorporating Time-Interval Dual Graphs for Recommender Systems
Nikita Severin, Savchenko A., Kiselev D. et al., , in: RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems.: Association for Computing Machinery (ACM), 2023.
Recommender systems are essential for personalized content delivery and have become increasingly popular recently. However, traditional recommender systems are limited in their ability to capture complex relationships between users and items. Dynamic graph neural networks (DGNNs) have recently emerged as a promising solution for improving recommender systems by incorporating temporal and sequential information in dynamic ...
Added: May 22, 2025
Research target: Computer Science
Language: English
DOI
Keywords: recommender systems
RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems
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