Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval

• URL: http://arxiv.org/abs/2505.22238v2
• Date: Sun, 01 Jun 2025 19:48:42 GMT
• Title: Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval
• Authors: A. Ploshkin, V. Tytskiy, A. Pismenny, V. Baikalov, E. Taychinov, A. Permiakov, D. Burlakov, E. Krofto, N. Savushkin,
• Abstract summary: We present Yambda-5B, a large-scale open dataset sourced from the Yandex Music streaming platform.<n>Yambda-5B contains 4.79 billion user-item interactions from 1 million users across 9.39 million tracks.<n>A key distinguishing feature of Yambda-5B is the inclusion of the is_organic flag, which separates organic user actions from recommendation-driven events.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Yambda-5B, a large-scale open dataset sourced from the Yandex Music streaming platform. Yambda-5B contains 4.79 billion user-item interactions from 1 million users across 9.39 million tracks. The dataset includes two primary types of interactions: implicit feedback (listening events) and explicit feedback (likes, dislikes, unlikes and undislikes). In addition, we provide audio embeddings for most tracks, generated by a convolutional neural network trained on audio spectrograms. A key distinguishing feature of Yambda-5B is the inclusion of the is_organic flag, which separates organic user actions from recommendation-driven events. This distinction is critical for developing and evaluating machine learning algorithms, as Yandex Music relies on recommender systems to personalize track selection for users. To support rigorous benchmarking, we introduce an evaluation protocol based on a Global Temporal Split, allowing recommendation algorithms to be assessed in conditions that closely mirror real-world use. We report benchmark results for standard baselines (ItemKNN, iALS) and advanced models (SANSA, SASRec) using a variety of evaluation metrics. By releasing Yambda-5B to the community, we aim to provide a readily accessible, industrial-scale resource to advance research, foster innovation, and promote reproducible results in recommender systems.

Related papers

• OmniGenBench: A Benchmark for Omnipotent Multimodal Generation across 50+ Tasks [77.19223035769248]
  Recent breakthroughs in large multimodal models (LMMs) have demonstrated remarkable proficiency in following general-purpose instructions for image generation.<n>We introduce OmniGenBench, a novel benchmark meticulously designed to assess the instruction-following abilities of state-of-the-art LMMs.<n>Our OmniGenBench includes 57 diverse sub-tasks grounded in real-world scenarios, systematically categorized according to the specific model capabilities they demand.
  arXiv  Detail & Related papers  (2025-05-24T16:29:34Z)
• Mind the Gap! Static and Interactive Evaluations of Large Audio Models [55.87220295533817]
  Large Audio Models (LAMs) are designed to power voice-native experiences.<n>This study introduces an interactive approach to evaluate LAMs and collect 7,500 LAM interactions from 484 participants.
  arXiv  Detail & Related papers  (2025-02-21T20:29:02Z)
• Context-aware adaptive personalised recommendation: a meta-hybrid [0.41436032949434404]
  We propose a meta-hybrid recommender that uses machine learning to predict an optimal algorithm. Based on the proposed model, it is possible to predict which recommender will provide the most precise recommendations to a user.
  arXiv  Detail & Related papers  (2024-10-17T09:24:40Z)
• Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settings [77.20838441870151]
  We use an online metric - the number of edits users introduce before committing the generated messages to the VCS - to select metrics for offline experiments.<n>We collect a dataset with 57 pairs consisting of commit messages generated by GPT-4 and their counterparts edited by human experts.<n>Our results indicate that edit distance exhibits the highest correlation with the online metric, whereas commonly used similarity metrics such as BLEU and METEOR demonstrate low correlation.
  arXiv  Detail & Related papers  (2024-10-15T20:32:07Z)
• On Generative Agents in Recommendation [58.42840923200071]
  Agent4Rec is a user simulator in recommendation based on Large Language Models. Each agent interacts with personalized recommender models in a page-by-page manner.
  arXiv  Detail & Related papers  (2023-10-16T06:41:16Z)
• UltraFeedback: Boosting Language Models with Scaled AI Feedback [99.4633351133207]
  We present textscUltraFeedback, a large-scale, high-quality, and diversified AI feedback dataset. Our work validates the effectiveness of scaled AI feedback data in constructing strong open-source chat language models.
  arXiv  Detail & Related papers  (2023-10-02T17:40:01Z)
• Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework [6.2405734957622245]
  Track2Vec is a GPU-free customizable-driven framework for fairness music recommendation. We introduce a metric called Miss Rate - Inverse Ground Truth Frequency (MR-ITF) to measure the fairness. Our model achieves a 4th price ranking in a GPU-free environment on the leaderboard in the EvalRS @ CIKM 2022 challenge.
  arXiv  Detail & Related papers  (2022-10-29T12:53:09Z)
• Click-Through Rate Prediction Using Graph Neural Networks and Online Learning [0.0]
  A small percent improvement on the CTR prediction accuracy has been mentioned to add millions of dollars of revenue to the advertisement industry. This project is interested in building a CTR predictor using Graph Neural Networks and an online learning algorithm.
  arXiv  Detail & Related papers  (2021-05-09T01:35:49Z)
• Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling [6.312395952874578]
  We consider the problem of recommending relevant content to users of an internet platform in the form of lists of items, called slates. We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user. We show experimentally that explorative recommender strategies perform on par or above their greedy counterparts.
  arXiv  Detail & Related papers  (2021-04-30T15:16:35Z)
• Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
  We formalize the recommender system as a sequential recommendation problem. We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
  arXiv  Detail & Related papers  (2020-05-19T10:18:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.