{"id":795614,"date":"2021-11-16T08:00:16","date_gmt":"2021-11-16T16:00:16","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=795614"},"modified":"2021-11-16T13:47:45","modified_gmt":"2021-11-16T21:47:45","slug":"research-talk-challenges-in-multi-tenant-graph-representation-learning-for-recommendation-problems","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/research-talk-challenges-in-multi-tenant-graph-representation-learning-for-recommendation-problems\/","title":{"rendered":"Research talk: Challenges in multi-tenant graph representation learning for recommendation problems"},"content":{"rendered":"<p>Recent research has shown that representations learned from user-user and user-item graphs can be used to improve recommendation performance. In this research, the recommendation model is often trained with representation learning. In project DEEGO, we aim to learn representations for various entities from multiple entity interaction graphs that can be used in various downstream recommendation scenarios. The various entities involved evolve at different rates. Additionally, the downstream recommendation scenarios may be either jointly trained along with the representation or trained in a way that is decoupled from representation learning. The latter is likely to be the most common scenario. These different nuances are challenges that we need to overcome to build a practical system. In this talk, we present the work that has been done so far and the challenges we need to solve in the future.<\/p>\n<p>Learn more about the 2021 Microsoft Research Summit: <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/Aka.ms\/researchsummit\">https:\/\/Aka.ms\/researchsummit<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recent research has shown that representations learned from user-user and user-item graphs can be used to improve recommendation performance. In this research, the recommendation model is often trained with representation learning. In project DEEGO, we aim to learn representations for various entities from multiple entity interaction graphs that can be used in various downstream recommendation [&hellip;]<\/p>\n","protected":false},"featured_media":795617,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13563,13555],"msr-video-type":[261263,261302],"msr-locale":[268875],"msr-post-option":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-795614","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-data-platform-analytics","msr-research-area-search-information-retrieval","msr-video-type-research-summit-2021","msr-video-type-the-future-of-search-recommendation","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/TWXpg2-HHpo","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/795614"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/795614\/revisions"}],"predecessor-version":[{"id":795623,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/795614\/revisions\/795623"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/795617"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=795614"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=795614"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=795614"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=795614"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=795614"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=795614"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=795614"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}