{"id":436691,"date":"2022-04-22T18:58:56","date_gmt":"2022-04-22T15:58:56","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/a-graph-convolution-machine-for-context-aware-recommender-systems\/"},"modified":"2022-04-22T18:58:56","modified_gmt":"2022-04-22T15:58:56","slug":"a-graph-convolution-machine-for-context-aware-recommender-systems","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/a-graph-convolution-machine-for-context-aware-recommender-systems\/","title":{"rendered":"#A graph convolution machine for context-aware recommender systems"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a23a67c1ed8e\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #dd3333;color:#dd3333\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #dd3333;color:#dd3333\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a23a67c1ed8e\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/buradabiliyorum.com\/en\/a-graph-convolution-machine-for-context-aware-recommender-systems\/#%E2%80%9CA_graph_convolution_machine_for_context-aware_recommender_systems%E2%80%9D\" >&#8220;A graph convolution machine for context-aware recommender systems&#8221;<\/a><\/li><\/ul><\/nav><\/div>\n<h1><span class=\"ez-toc-section\" id=\"%E2%80%9CA_graph_convolution_machine_for_context-aware_recommender_systems%E2%80%9D\"><\/span>&#8220;A graph convolution machine for context-aware recommender systems&#8221;<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<div>\n<div class=\"article-gallery lightGallery\">\n<div data-thumb=\"https:\/\/scx1.b-cdn.net\/csz\/news\/tmb\/2022\/graph-convolution-mach.jpg\" data-src=\"https:\/\/scx2.b-cdn.net\/gfx\/news\/hires\/2022\/graph-convolution-mach.jpg\" data-sub-html=\"The data used for building a CARS. The mixture data of interaction tensor and user\/item\/context feature matrices are converted to an attributed user-item bipartite graph without loss of fidelity. Credit: Higher Education Press Limited Company\">\n<figure class=\"article-img\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/scx1.b-cdn.net\/csz\/news\/800a\/2022\/graph-convolution-mach.jpg\" alt=\"Graph convolution machine for context-aware recommender system\" title=\"The data used for building a CARS. The mixture data of interaction tensor and user\/item\/context feature matrices are converted to an attributed user-item bipartite graph without loss of fidelity. Credit: Higher Education Press Limited Company\" width=\"800\" height=\"302\"\/><figcaption class=\"text-darken text-low-up text-truncate-js text-truncate mt-3\">\n                The data used for building a CARS. The mixture data of interaction tensor and user\/item\/context feature matrices are converted to an attributed user-item bipartite graph without loss of fidelity. Credit: Higher Education Press Limited Company<br \/>\n            <\/figcaption><\/figure>\n<\/p><\/div>\n<\/div>\n<p>The latest advance in recommendation <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/technology\/\" data-internallinksmanager029f6b8e52c=\"4\" title=\"Technology\" target=\"_blank\" rel=\"noopener\">technology<\/a> shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such a finding is mostly restricted to the collaborative filtering (CF) scenario, where the interaction contexts are not available.<\/p>\n<p>                                                                                To extend the advantages of graph convolutions to context-aware recommender systems (CARSs), which represents a generic type of models that can handle various side information, a research team led by Xiangnan HE published their new research on January 22nd, 2022 in <i>Frontiers of Computer <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/sciencee\/\" data-internallinksmanager029f6b8e52c=\"5\" title=\"Science\" target=\"_blank\" rel=\"noopener\">Science<\/a><\/i>.<\/p>\n<p>The team developed a new model, GCM, which captures the interactions among multiple user behaviors via graph neural networks, and then models the interactions among features of individual behavior via factorization machine. To demonstrate the effectiveness of GCM, they test it on three public datasets. Extensive experiments also are conducted to verify the the rationality of the attributed graph and offer insights into how the representations benefit from such graph learning.<\/p>\n<p>Organizing user behaviors with contextual information in graphs is a promising direction to build an effective context-aware recommender. It helps build strong representations for users and items. GCM simply unifies all context features as an edge, neglecting the dynamic characteristics of some contexts (e.g., time) and hardly capturing the dynamic preference of users. Future work could be done on building dynamic graphs based on contextual information instead of one static graph, or devising a dynamic graph neural network.<\/p>\n<div class=\"article-gallery lightGallery\">\n<div data-thumb=\"https:\/\/scx1.b-cdn.net\/csz\/news\/tmb\/2022\/graph-convolution-mach-1.jpg\" data-src=\"https:\/\/scx2.b-cdn.net\/gfx\/news\/hires\/2022\/graph-convolution-mach-1.jpg\" data-sub-html=\"The graph convolution machine model. Credit: Higher Education Press Limited Company\">\n<figure class=\"article-img text-center\">\n            <img decoding=\"async\" src=\"https:\/\/scx1.b-cdn.net\/csz\/news\/800a\/2022\/graph-convolution-mach-1.jpg\" alt=\"Graph convolution machine for context-aware recommender system\"\/><figcaption class=\"text-left text-darken text-truncate text-low-up mt-3\">\n                The graph convolution machine model. Credit: Higher Education Press Limited Company<br \/>\n            <\/figcaption><\/figure>\n<\/p><\/div>\n<\/div>\n<hr\/>\n<div class=\"article-main__explore my-4 d-print-none\">\n<p>                                            A hierarchical RNN-based model to predict scene graphs for images\n                                        <\/p><\/div>\n<hr class=\"mb-4\"\/>\n<div class=\"article-main__more p-4\">\n                                                                                                <strong>More information:<\/strong><br \/>\n                                                Jiancan Wu et al, Graph convolution machine for context-aware recommender system, <i>Frontiers of Computer Science<\/i> (2022).  <a rel=\"nofollow noopener\" target=\"_blank\" data-doi=\"1\" href=\"https:\/\/dx.doi.org\/10.1007\/s11704-021-0261-8\">DOI: 10.1007\/s11704-021-0261-8<\/a><\/p><\/div>\n<p>                                                Provided by<br \/>\n                                                                                                    Higher Education Press<\/p>\n<p>                                        <!-- print only --><\/p>\n<div class=\"d-none d-print-block\">\n<p>                                                 <strong>Citation<\/strong>:<br \/>\n                                                 A graph convolution machine for context-aware recommender systems (2022, April 22)<br \/>\n                                                 retrieved 24 April 2022<br \/>\n                                                 from https:\/\/techxplore.com\/<a href=\"https:\/\/buradabiliyorum.com\/en\/category\/news\/\" data-internallinksmanager029f6b8e52c=\"2\" title=\"News\" target=\"_blank\" rel=\"noopener\">news<\/a>\/2022-04-graph-convolution-machine-context-aware.html<\/p>\n<p>                                            This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no<br \/>\n                                            part may be reproduced without the written permission. The content is provided for information purposes only.<\/p><\/div>\n<\/p><\/div>\n<p><script id=\"facebook-jssdk\" async=\"\" src=\"https:\/\/connect.facebook.net\/en_US\/sdk.js\"><\/script><\/p>\n<blockquote><p><strong><span style=\"color: #ff6600;\">If you liked the article, do not forget to share it with your friends. 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The mixture data of interaction tensor and user\/item\/context feature matrices are converted to an attributed user-item bipartite graph without loss of fidelity. Credit: Higher Education Press Limited Company The latest advance in recommendation technology shows that better user and item&#8230;<\/p>\n","protected":false},"author":1,"featured_media":436692,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/scx2.b-cdn.net\/gfx\/news\/hires\/2022\/graph-convolution-mach.jpg","fifu_image_alt":"","footnotes":""},"categories":[16],"tags":[],"class_list":["post-436691","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sciencee"],"_links":{"self":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts\/436691","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/comments?post=436691"}],"version-history":[{"count":0,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts\/436691\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/media\/436692"}],"wp:attachment":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/media?parent=436691"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/categories?post=436691"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/tags?post=436691"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}