{"url":"/dataset/e-commerce-1","name":"E-commerce","full_name":"E-commerce","description_markdown":"We release E-commerce Dialogue Corpus, comprising a training data set, a development set and a test set for retrieval based chatbot. The statistics of E-commerical Conversation Corpus are shown in the following table. \r\n\r\n|      |Train|Val| Test         |\r\n| ------------- |:-------------:|:-------------:|:-------------:|\r\n| Session-response pairs  | 1m|10k| 10k |\r\n| Avg. positive response per session|1|1|1|\r\n| Min turn per session|3|3|3|\r\n| Max ture per session|10|10|10|\r\n| Average turn per session|5.51|5.48|5.64\r\n| Average Word per utterance|7.02|6.99|7.11\r\n\r\nThe full corpus can be downloaded from https://drive.google.com/file/d/154J-neBo20ABtSmJDvm7DK0eTuieAuvw/view?usp=sharing.","description_withheld":null,"homepage":"https://github.com/cooelf/DeepUtteranceAggregation","introduced_date":"2018-06-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/modeling-multi-turn-conversation-with-deep","title":"Modeling Multi-turn Conversation with Deep Utterance Aggregation","first_author":"Zhuosheng Zhang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Conversational Response Selection","url":"/task/conversational-response-selection","datasets_with_task":"/datasets/task/conversational-response-selection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["E-commerce"],"data_loaders":[],"num_papers_in_archive":42,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/conversational-response-selection-on-e","task":"Conversational Response Selection","dataset_variant":"E-commerce","rows":15,"metrics":["R10@1","R10@2","R10@5"],"first_row_in_archive_order":{"model":"BERT-FP+EDHNS","paper":"/paper/efficient-dynamic-hard-negative-sampling-for","metrics":{"R10@1":"0.957","R10@2":"0.986","R10@5":"0.997"},"code_links":[{"title":"hanjanghoon/EDHNS","url":"https://github.com/hanjanghoon/EDHNS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/efficient-dynamic-hard-negative-sampling-for","title":"Efficient Dynamic Hard Negative Sampling for Dialogue Selection","date":"2024-08-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/contextual-masked-auto-encoder-for-retrieval","title":"Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems","date":"2023-06-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/two-level-supervised-contrastive-learning-for-1","title":"Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue","date":"2022-03-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/fine-grained-post-training-for-improving","title":"Fine-grained Post-training for Improving Retrieval-based Dialogue Systems","date":"2021-05-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dialogue-response-selection-with-hierarchical","title":"Dialogue Response Selection with Hierarchical Curriculum Learning","date":"2020-12-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-an-effective-context-response","title":"Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues","date":"2020-09-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/do-response-selection-models-really-know-what","title":"Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection","date":"2020-09-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/speaker-aware-bert-for-multi-turn-response","title":"Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots","date":"2020-04-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/grayscale-data-construction-and-multi-level","title":"The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection","date":"2020-04-06","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/utterance-to-utterance-interactive-matching","title":"Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots","date":"2019-11-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-hop-selector-network-for-multi-turn","title":"Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots","date":"2019-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/one-time-of-interaction-may-not-be-enough-go","title":"One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues","date":"2019-07-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/interactive-matching-network-for-multi-turn","title":"Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots","date":"2019-01-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/modeling-multi-turn-conversation-with-deep","title":"Modeling Multi-turn Conversation with Deep Utterance Aggregation","date":"2018-06-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sequential-matching-network-a-new","title":"Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots","date":"2016-12-06","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}