{"total":27,"rows":[{"id":"2101.07361","title":"Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification","created":"2021-01-18","cited_by_count":13,"area":null},{"id":"2406.10173","title":"IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce","created":"2024-06-14","cited_by_count":11,"area":null},{"id":"2104.02061","title":"Query2Prod2Vec Grounded Word Embeddings for eCommerce","created":"2021-04-02","cited_by_count":8,"area":null},{"id":"2104.08874","title":"Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction","created":"2021-04-18","cited_by_count":5,"area":null},{"id":"2102.12029","title":"Theoretical Understandings of Product Embedding for E-commerce Machine Learning","created":"2021-02-24","cited_by_count":4,"area":null},{"id":"2105.08585","title":"Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings","created":"2021-05-18","cited_by_count":4,"area":null},{"id":"2107.03256","title":"\"Are you sure?\": Preliminary Insights from Scaling Product Comparisons to Multiple Shops","created":"2021-07-07","cited_by_count":4,"area":null},{"id":"2203.14037","title":"Data Augmentation Strategies for Improving Sequential Recommender Systems","created":"2022-03-26","cited_by_count":4,"area":null},{"id":"2112.11022","title":"Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions","created":"2021-12-21","cited_by_count":2,"area":null},{"id":"2211.14982","title":"Two Is Better Than One: Dual Embeddings for Complementary Product Recommendations","created":"2022-11-28","cited_by_count":2,"area":null},{"id":"2106.07287","title":"Data Science Methodologies: Current Challenges and Future Approaches","created":"2021-06-14","cited_by_count":1,"area":null},{"id":"2107.01529","title":"Learning Complex Users' Preferences for Recommender Systems","created":"2021-07-04","cited_by_count":1,"area":null},{"id":"2205.06893","title":"Large-Scale Sequential Learning for Recommender and Engineering Systems","created":"2022-05-13","cited_by_count":1,"area":null},{"id":"2401.16433","title":"Within-basket Recommendation via Neural Pattern Associator","created":"2024-01-25","cited_by_count":1,"area":null},{"id":"2506.22648","title":"Interact2Vec -- An efficient neural network-based model for simultaneously learning users and items embeddings in recommender systems","created":"2025-06-27","cited_by_count":1,"area":null},{"id":"2104.07748","title":"Variational Inference for Category Recommendation in E-Commerce platforms","created":"2021-04-15","cited_by_count":0,"area":null},{"id":"2108.13824","title":"Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation","created":"2021-08-31","cited_by_count":0,"area":null},{"id":"2202.13240","title":"Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)","created":"2022-02-26","cited_by_count":0,"area":null},{"id":"2210.16590","title":"Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework","created":"2022-10-29","cited_by_count":0,"area":null},{"id":"2211.09174","title":"CASPR: Customer Activity Sequence-based Prediction and Representation","created":"2022-11-16","cited_by_count":0,"area":null},{"id":"2301.07946","title":"Job recommendations: benchmarking of collaborative filtering methods for classifieds","created":"2023-01-19","cited_by_count":0,"area":null},{"id":"2311.02084","title":"ITEm: Unsupervised Image-Text Embedding Learning for eCommerce","created":"2023-10-22","cited_by_count":0,"area":null},{"id":"2312.08393","title":"Multi-criteria recommendation systems to foster online grocery","created":"2023-12-12","cited_by_count":0,"area":null},{"id":"2408.08024","title":"Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx","created":"2024-08-15","cited_by_count":0,"area":null},{"id":"2506.09209","title":"Revisiting Graph Projections for Effective Complementary Product Recommendation","created":"2025-06-10","cited_by_count":0,"area":null},{"id":"2508.20024","title":"Using item recommendations and LLMs in marketing email titles","created":"2025-08-27","cited_by_count":0,"area":null},{"id":"2604.15581","title":"Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts","created":"2026-04-16","cited_by_count":0,"area":null}],"next_cursor":null}