Aleksandr V. Petrov
@asash
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Senior Scientist (IR/RecSys/ML) @Tripadvisor | PhD @ University of Glasgow | Ex. Senior Software Engineer @Amazon | The opinions are mine
Glasgow
Joined January 2009
RT @craig_macdonald: Thrilled to join @asash on the Recsperts podcast! Thanks @MarcelKurovski for having us. We had a blast discussing our….
recsperts.com
In episode 29 of Recsperts, I welcome Craig Macdonald, Professor of Information Retrieval at the University of Glasgow, and Aleksandr “Sasha” Petrov, PhD researcher and former applied scientist at ...
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RT @ir_glasgow: Huge congratulations to @macavaney on receiving the prestigious ACM SIGIR Early Career Researcher Award in the research cat….
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RT @alberto_mancino: 2nd day at #SIGIR2025 ☀️.After the first keynote, a great talk by @asash on combining joint product quantization and d….
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Just presented our work at #SIGIR2025. @craig_macdonald gave a great overview. If you have any questions, feel free to catch me during the coffee break!.
.@Asash is talking about our #sigir2025 📄 applying dynamic pruning ideas in sequential recommended systems, using sub-id representations w/ myself and @ntonellotto
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If you're at #SIGIR2025 and interested in large-scale RecSys, pop by my talk on Monday! I'll be presenting our paper (w/ @craig_macdonald and @ntonellotto): 'Efficient Recommendation with Millions of Items by Dynamic Pruning of Sub-Item Embeddings'. 🔗
arxiv.org
A large item catalogue is a major challenge for deploying modern sequential recommender models, since it makes the memory footprint of the model large and increases inference latency. One...
📢 We're off to #SIGIR2025 in Padova!. A large contingent of our students & staff will be at the main conference + tutorials, workshops & #ICTIR2025. Let’s connect if you’re around! 🤝. Also: we're hiring in #FinTech — DM us if you're interested!.#IR #Hiring #Research #ACMSIGIR
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RT @ir_glasgow: Huge congratulations to our brilliant PhD graduates: @tjaenich, @mvlacho1, @asash - It’s been a joy having you @GlasgowCS.….
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RT @WSDMSocial: We’re excited to announce that WSDM 2026 will take place in Boise, Idaho, from February 22 to 26, 2026!.Stay tuned and visi….
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Happy to share that the OARS@KDD2025 workshop accepts our work. In this work, we turn a sequential recommendation system into a semantic search using generative language models. Apparently, this works really well.
1/9 Happy to share that our paper GLoSS: Generative Language Models with Semantic Search for.Sequential Recommendation is accepted at the KDD OARS workshop! 🎉.Paper, code: This is joint work with my wonderful collaborators @asash and Juba Ziani.
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Thanks to everyone involved! Extremely happy that no corrections required!.
Delighted that @asash passed his 🎓 PhD defense this morning, without corrections. Thanks to @pcastells and Nicolas Pugeault for their thorough examination of the thesis, and @mobilelearnfeed for convening the defense!
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RT @craig_macdonald: I'm delighted to announce that I'm joining hashtag @Amazon Edinburgh as a part-time Visiting Scholar, working on recom….
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The pre-print of our #SIGIR2025 paper is now available at arXiv: . /w @craig_macdonald & @ntonellotto.
arxiv.org
A large item catalogue is a major challenge for deploying modern sequential recommender models, since it makes the memory footprint of the model large and increases inference latency. One...
Efficient Recommendation with Millions of Items by Dynamic Pruning of Sub-Item Embeddings. @asash et al. introduce a dynamic pruning algorithm that efficiently finds top-K items without computing scores for the entire catalogue. 📝👨🏽💻
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RT @ir_glasgow: Arrivederci @ecir2025 #ecir2025 … and thanks for all the fish - see you all in Delft.
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RT @ir_glasgow: The last remaining members of the group at the closing of the #ir4good track and the #ecir2025 workshops. As we bid farewel….
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RT @ir_glasgow: Our own @asash has just finished presenting his paper entitled ‘CountNet: Utilising Repetition Counts in Sequential Recomm….
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The paper continues our line of work on Large-Scale Sequential Recommendation. In this work we show that it is possible to find exact TOP-K highest scored items without exhaustively scoring full catalogue.
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Great news from #SIGIR2025 — our (/w @craig_macdonald and @ntonellotto) full paper “Efficient Recommendation with Millions of Items by Dynamic Pruning of Sub-Item Embeddings” has been accepted. Nice timing too: all co-authors are now at ECIR, so we get to celebrate together.
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See you soon in Lucca!.
A large contingent of our staff & students @GlasgowCS will soon be traveling to #ECIR2025. We'll be presenting our latest work in #search & #RecSys throughout the 5-day conference. Looking forward to connecting with colleagues and friends in Lucca!.@ecir2025 @IDAglasgow
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