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GPT3RecBot: a universal chatbot recommender of movies, books and music in Telegram
P. 35–43.
Lashinin O., Bykov K., Ananyeva M., Kolesnikov S.
Recent advances in large language models have extended their potential use cases to different domains. Models such as ChatGPT have an extensive internal knowledge base that enables them to provide answers to various domain-specific queries. In this paper, we explore the potential use of OpenAI’s GPT3.5 model as a conversational recommender system. We designed a user-friendly chatbot capable of recommending items in three domains: books, movies, and music. Our study involved collecting explicit feedback from 517 users, and we report the results obtained. The average usefulness of our bot is 4.15 / 5. Our experimental results demonstrate the effectiveness of GPT3.5 as a personalised recommendation system. We hope that our work will inspire further research in this area. Our chatbot is available on the popular messaging platform Telegram under the name @GPT3Recbot, making it accessible to a wide range of users.
In book
Vol. 3560. , CEUR Workshop Proceedings, 2023.
Sorokin I., Tekic Z., Technology in Society 2026 Vol. 88 Article 103456
Generative AI (GenAI) digital products challenge established new product development and product success frameworks because performance after launch remains variable: outputs differ across runs and contexts, behavior can shift under drift, and realized value depends on user interaction, oversight, and governance at scale. Focusing on large language model (LLM)-based GenAI digital products, this study identifies ...
Added: September 23, 2026
Rezunik L., Alexandrov D., Mikhail Prozorskiy, , in: Intelligent Decision Technologies. Proceedings of the 17th KES-IDT 2025 ConferenceVol. 450.: Cham: Springer, 2026. P. 81–91.
Multi-Agent Systems (MAS) can benefit from Large Language Models (LLMs), but hallucinations pose risks to decision-making. This paper introduces an approach for creating MAS based on LLMs and proposes a generalized architecture for such systems. We ensure that reasoning is conducted through predicate logic to minimize errors, and LLMs are exclusively utilized to translate natural ...
Added: September 14, 2026
Grishina E., Stepan Kuznetsov, Tsyganov A. et al., , in: KDD '26: Proceedings of the 32th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.: Association for Computing Machinery (ACM), 2026. P. 1310–1321.
The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale. This drives a demand for a proper methodology for fair comparison between algorithms. Naive aggregation of performance metrics (e.g., averaging NDCG over benchmarks) can yield misleading rankings, undermining practical selection. To ...
Added: August 7, 2026
Buzaev F., Mullakhmetov R., Bogachev R. et al., Association for Computational Linguistics, 2026.
Playlist generation based on textual queries using large language models (LLMs) is becoming an important interaction paradigm for music streaming platforms. User queries span a wide spectrum from highly personalized intent to essentially catalog-style requests. Existing systems typically rely on non-personalized retrieval/ranking or apply a fixed level of preference conditioning to every query, which can ...
Added: June 22, 2026
Stepin A., Mozikov M., Kabanov A. et al., IEEE Access 2026 Vol. 14 P. 48127–48144
The deployment of large language models (LLMs) in interactive roles such as automated negotiators, customer service agents, and strategic partners requires them to handle not only logical tasks but also the socio-emotional dimensions of interaction. In these situations, success often relies on understanding social cues, building trust, and using persuasion effectively. These skills are closely ...
Added: June 16, 2026
Tyuryakova-Matveeva D., Цифровые гуманитарные исследования 2026 № 1 С. 4–26
The article explores cultural references in the works of Andrei Voznesensky by analyzing the personalities he mentions. A total of 1,678 works were processed, including poetry, prose, and early unpublished poems. NER methods based on Natasha, spaCy, and LLM Grok tools made it possible to study the frequency of mentions of famous people and their ...
Added: May 31, 2026
Beknazarov N., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 3–16.
This paper addresses the challenge of efficiently training Large Language Models (LLMs) on large-scale, sparse omics datasets in high-performance computing (HPC) environments. Using over 1000 BED tracks as a representative data source, we propose a method combining interval-based chunked storage, sparse matrix transformation, and parallel data loading, integrated within a PyTorch Lightning training framework. Our ...
Added: May 19, 2026
Yusupov V., Sukhorukov N., Frolov E., User Modelling and User-Adapted Interaction 2026 Vol. 36 Article 2
Graph-based recommender systems have emerged as a powerful paradigm for personalized recommendations. However, their reliance on full model retraining to incorporate new users or new interactions creates scalability barriers. The task becomes infeasible in real-life recommender systems due to excessive time and resource costs involved. To address this limitation, we propose a fast and efficient ...
Added: March 15, 2026
Seleznyov M., Chaichuk M., Ershov G. et al., , in: Findings of the Association for Computational Linguistics: EMNLP 2025.: Association for Computational Linguistics, 2025. P. 20370–20385.
Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and formatting. In this work, we present the first systematic evaluation of 4 methods for improving prompt robustness within a unified experimental framework. We benchmark these techniques on 8 models from Llama, Qwen and Gemma families across 52 tasks from Natural ...
Added: February 3, 2026
Ganeeva V., Khrabrov K., Kadurin A. et al., Journal of Cheminformatics 2025 No. 17 Article 164
The recent integration of natural language processing into chemistry has advanced drug discovery. Molecule representations in language models (LMs) are crucial to enhance chemical understanding. We explored the ability of models to match the same chemical structures despite their different representations. Recognizing the same substance in different representations is an important component of emulating the ...
Added: February 3, 2026
Klenitskiy A., Anna Volodkevich, Pembek A. et al., ACM Transactions on Recommender Systems 2026
Sequential recommender systems are an important and in-demand area of research. These systems aim to use the order of interactions in a user’s history to predict future interactions. The premise is that the order of interactions and sequential patterns play an essential role. Therefore, it is crucial to use datasets that exhibit a sequential structure ...
Added: January 28, 2026
Anna Volodkevich, Danil Gusak, Klenitskiy A. et al., User Modelling and User-Adapted Interaction 2025 No. 35 Article 13
The goal of modern sequential recommender systems is often formulated in terms of next-item prediction. In this paper, we explore the applicability of transformer-based generative models for the Top-K sequential recommendation task, where the goal is to predict items that a user is likely to interact with in the “near future.” This goal aligns with ...
Added: January 26, 2026
Klenitskiy A., Fatkulin A., Denisova D. et al., , in: RecSysChallenge '25: Proceedings of the Recommender Systems Challenge 2025.: Association for Computing Machinery (ACM), 2025. P. 26–30.
Building universal user representations that capture the essential aspects of user behavior is a crucial task for modern machine learning systems. In real-world applications, a user’s historical interactions often serve as the foundation for solving a wide range of predictive tasks, such as churn prediction, recommendations, or lifetime value estimation. Using a task-independent user representation ...
Added: January 26, 2026
Ivanova V., Frolov E., Vasilev A., , in: RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems.: ACM, 2025. P. 1142–1147.
We consider the task of learning from both positive and negative feedback in a sequential recommendation scenario, as both types of feedback are often present in user interactions. Meanwhile, conventional sequential learning models usually focus on considering and predicting positive interactions, ignoring that reducing items with negative feedback in recommendations improves user satisfaction with the ...
Added: January 26, 2026
Pembek A., Fatkulin A., Klenitskiy A. et al., , in: RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems.: ACM, 2025. P. 626–631.
Many sequential recommender systems suffer from the cold start problem, where items with few or no interactions cannot be effectively used by the model due to the absence of a trained embedding. Content-based approaches, which leverage item metadata, are commonly used in such scenarios. One possible way is to use embeddings derived from content features ...
Added: January 26, 2026
Anastasia V. Kolmogorova, Elizaveta R. Kulikova, Vladislav V. Lobanov, Supercomputing Frontiers and Innovations 2025 Vol. 12 No. 3 P. 121–140
Museum reviews provide rich insight into visitor preferences and can drive useful change within institutions, yet they have attracted little attention in sentiment research owing to limited commercial interest and the multi-thematic nature of reviews. In this study we analysed over 12 000 reviews in Russian for 15 museum sites collected from nine different platforms. ...
Added: November 30, 2025
Khrylchenko K., Matveev A., Makeev S. et al., , in: KDD '26: Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1Vol. 1.: Association for Computing Machinery (ACM), 2026. P. 2255–2265.
While large transformer models have been successfully used in many real-world applications such as natural language processing, computer vision, and speech processing, scaling transformers for recommender systems remains a challenging problem. Recently, Generative Recommenders framework was proposed to scale beyond typical Deep Learning Recommendation Models (DLRMs). Reformulation of recommendation as sequential transduction task led to ...
Added: November 25, 2025
Association for Computing Machinery (ACM), 2026.
KDD is the premier Data Science and AI conference, hosting both a Research and an Applied Data Science Track. The conference will take place from August 9 to 13, 2026, in Jeju, Korea. ...
Added: November 25, 2025
Makeev S., Andreev A., Baikalov V. et al., , in: RecSysChallenge '25: Proceedings of the Recommender Systems Challenge 2025.: Association for Computing Machinery (ACM), 2025. P. 21–25.
This paper describes the 4th-place solution by team ambitious for the RecSys Challenge 2025, organized by Synerise and ACM RecSys, which focused on universal behavioral modeling. The challenge objective was to generate user embeddings effective across six diverse downstream tasks. Our solution integrates (1) a sequential encoder to capture the temporal evolution of user interests, (2) a ...
Added: November 19, 2025
Association for Computing Machinery (ACM), 2025.
Added: November 19, 2025
Khrylchenko K., Baikalov V., Makeev S. et al., , in: RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems.: ACM, 2025. P. 545–550.
Added: November 19, 2025