{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-latent-vector-spaces-for-product","title":"Learning Latent Vector Spaces for Product Search","arxiv_id":"1608.07253","date":"2016-08-25","proceeding":null,"authors":["Christophe Van Gysel","Maarten de Rijke","Evangelos Kanoulas"],"abstract":"We introduce a novel latent vector space model that jointly learns the latent\nrepresentations of words, e-commerce products and a mapping between the two\nwithout the need for explicit annotations. The power of the model lies in its\nability to directly model the discriminative relation between products and a\nparticular word. We compare our method to existing latent vector space models\n(LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank\nsetting. Our latent vector space model achieves its enhanced performance as it\nlearns better product representations. Furthermore, the mapping from words to\nproducts and the representations of words benefit directly from the errors\npropagated back from the product representations during parameter estimation.\nWe provide an in-depth analysis of the performance of our model and analyze the\nstructure of the learned representations.","url_abs":"http://arxiv.org/abs/1608.07253v1","url_pdf":"http://arxiv.org/pdf/1608.07253v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-latent-vector-spaces-for-product","repo_url":"https://github.com/cvangysel/SERT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"learning-latent-vector-spaces-for-product","repo_url":"https://github.com/cvangysel/cuNVSM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"learning-to-rank","task_name":"Learning-To-Rank"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.07253","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}