{"id":743947,"date":"2021-05-05T06:01:22","date_gmt":"2021-05-05T13:01:22","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=743947"},"modified":"2021-05-05T06:01:22","modified_gmt":"2021-05-05T13:01:22","slug":"deep-distribution-network-addressing-the-data-sparsity-issue-for-top-n-recommendation","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/deep-distribution-network-addressing-the-data-sparsity-issue-for-top-n-recommendation\/","title":{"rendered":"Deep Distribution Network: Addressing the Data Sparsity Issue for Top-N Recommendation"},"content":{"rendered":"<div class=\"hlFld-Abstract\">\n<div class=\"article__section article__abstract hlFld-Abstract\">\n<div class=\"abstractSection abstractInFull\">\n<p>Existing recommendation methods mostly learn fixed vectors for users and items in a low-dimensional continuous space, and then calculate the popular dot-product to derive user-item distances. However, these methods suffer from two drawbacks: (1) the data sparsity issue prevents from learning high-quality representations; and (2) the dot-product violates the crucial triangular inequality and therefore, results in a sub-optimal performance. In this work, in order to overcome the two aforementioned drawbacks, we propose Deep Distribution Network (DDN) to model users and items via Gaussian distributions. We argue that, compared to fixed vectors, distribution-based representations are more powerful to characterize users&#8217; uncertain interests and items&#8217; distinct properties. In addition, we propose a Wasserstein-based loss, in which the critical triangular inequality can be satisfied. In experiments, we evaluate DDN and comparative models on standard datasets. It is shown that DDN significantly outperforms state-of-the-art models, demonstrating the advantages of the proposed distribution-based representations and wassertein loss.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"pb-dropzone\" data-pb-dropzone=\"pubContentAccessDenialDropzone\"><\/div>\n<div class=\"article__section article__references show-more-items js--showMore\" data-sectionname=\"References\">\n<div class=\"colored-block__title\"><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Existing recommendation methods mostly learn fixed vectors for users and items in a low-dimensional continuous space, and then calculate the popular dot-product to derive user-item distances. However, these methods suffer from two drawbacks: (1) the data sparsity issue prevents from learning high-quality representations; and (2) the dot-product violates the crucial triangular inequality and therefore, results [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"SIGIR","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2019-7-1","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":false,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13555],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-743947","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-7-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":0,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3331184.3331330","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Lei Zheng","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Chaozhuo Li","user_id":39054,"rest_url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Chaozhuo Li"},{"type":"text","value":"Chun-Ta Lu","user_id":0,"rest_url":false},{"type":"text","value":"Jiawei Zhang","user_id":0,"rest_url":false},{"type":"text","value":"Philip S. 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