{"id":12723,"date":"2024-09-30T05:53:56","date_gmt":"2024-09-30T05:53:56","guid":{"rendered":"https:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-cross-modal-fusion-and-attention-mechanism-for-weakly-supervised-video-anomaly-detection-copy\/"},"modified":"2024-11-19T10:22:53","modified_gmt":"2024-11-19T10:22:53","slug":"summarizing-enhancing-social-recommendation-with-multi-view-bert-network-mvbn","status":"publish","type":"post","link":"https:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-enhancing-social-recommendation-with-multi-view-bert-network-mvbn\/","title":{"rendered":"Summarizing \u2018Enhancing Social Recommendation with Multi-View BERT Network (MVBN)\u2019"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"12723\" class=\"elementor elementor-12723\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cd44eb5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cd44eb5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9f11b70\" data-id=\"9f11b70\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-215a70e elementor-widget elementor-widget-heading\" data-id=\"215a70e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">BLOGS<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-28dc161 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"28dc161\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-63cf269\" data-id=\"63cf269\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6837436 elementor-widget elementor-widget-heading\" data-id=\"6837436\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Summarizing \u2018Enhancing Social Recommendation with Multi-View BERT Network (MVBN)\u2019<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9bd1630 elementor-widget elementor-widget-text-editor\" data-id=\"9bd1630\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTushar Prakash, Raksha Jalan, Onoe Naoyuki\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a034cb elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"7a034cb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>30<sup>th<\/sup> September 2024<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7d1e72 elementor-widget elementor-widget-image\" data-id=\"a7d1e72\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/elementor\/thumbs\/blog1-quuumsmhlvqrxfre0mcftd9ehyv00e64ikhm6f7b4q.png\" title=\"blog1\" alt=\"blog1\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" class=\"lazyload\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/489;\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-acbeaeb elementor-widget elementor-widget-text-editor\" data-id=\"acbeaeb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Overview of proposed framework<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9202657 elementor-widget elementor-widget-text-editor\" data-id=\"9202657\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Tushar Prakash summarises paper titled <\/span><a href=\"https:\/\/www.computer.org\/csdl\/proceedings-article\/icdm\/2023\/078800b277\/1Ui3xQYuC9G\" target=\"_blank\"><span style=\"font-weight: 400;\">Enhancing Social Recommendation with Multi-View BERT Network<\/span><\/a><span style=\"font-weight: 400;\"> co-authored by Raksha Jalan and Naoyuki Onoe. Accepted at <\/span><a href=\"https:\/\/www.cloud-conf.net\/icdm2023\/\"  target=\"_blank\"><span style=\"font-weight: 400;\">23rd IEEE International Conference on Data Mining (ICDM)<\/span><\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f0a3e28 elementor-widget elementor-widget-text-editor\" data-id=\"f0a3e28\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><b>Introduction<\/b><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d95d9a3 elementor-widget elementor-widget-text-editor\" data-id=\"d95d9a3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>E-commerce and online entertainment platforms increasingly rely on recommendation systems to filter irrelevant content and offer personalized recommendations. With features like Disney+\u2018s \u201cGroupWatch\u201d and Amazon Prime Video\u2019s \u201cWatch Party,\u201d there\u2019s a growing focus on incorporating social relationships into these models. However, the complexity of social dynamics makes it challenging to accurately model user preferences. Traditional approaches, such as Matrix Factorization and deep learning models, attempt to incorporate social relations but often assume shared interests among connected users, overlooking the diverse factors that influence preferences. These models also tend to focus on one-sided interactions, failing to capture a comprehensive, context-aware understanding.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-27a8d90 elementor-widget elementor-widget-text-editor\" data-id=\"27a8d90\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTo address these issues, we proposed MVBN (Multi-View BERT Network), which contains the following key components:\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a51851b elementor-widget elementor-widget-text-editor\" data-id=\"a51851b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Neighbourhood Sampling:<\/b><span style=\"font-weight: 400;\"> Selects influential neighbors based on social behavior and interactions to enhance user and item embeddings.<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Sequence Masking:<\/b><span style=\"font-weight: 400;\"> Creates sequences of user-interacted items and masks some items to form a prediction task, refining embeddings through sampling to capture correlations.\n<\/span><\/li>\n\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Item and User Views:<\/b><span style=\"font-weight: 400;\"> The model incorporates both item view (user\u2019s interaction history and item similarity) and user view (social connections, user-user similarities, and network data) to generate embeddings that reflect hidden preferences, enabling a more context-aware recommendation system.\n<\/span><\/li>\n\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8140b59 elementor-widget elementor-widget-text-editor\" data-id=\"8140b59\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>By integrating these item and user views, the model provides a richer understanding of both user preferences and social influences. A shared BERT network processes masked sequences through Transformer layers, and multi-task learning is employed to predict masked items while refining user embeddings. This setup improves both the efficiency and overall performance of the recommendation system.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-580c6ea elementor-widget elementor-widget-text-editor\" data-id=\"580c6ea\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><b>Key Results<\/b><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e94b615 elementor-widget elementor-widget-image\" data-id=\"e94b615\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/elementor\/thumbs\/blog2-quuut43hzhxw9w7va11kqpvnk78oivq8mgjsk9qoh0.png\" title=\"blog2\" alt=\"blog2\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" class=\"lazyload\" style=\"--smush-placeholder-width: 1000px; --smush-placeholder-aspect-ratio: 1000\/1162;\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3b2614d elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"3b2614d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Table: Comparison with state-of-the-art methods on XD-Violence Dataset<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c00888d elementor-widget elementor-widget-text-editor\" data-id=\"c00888d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The results in Tables 3 and 4 display MVBN&#8217;s superior performance across all three datasets compared to baselines, underscoring the effectiveness of our BERT and Multi-task learning embedding layer for processing user-item interactions and social relations. From these tables, we note:<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0fcf170 elementor-widget elementor-widget-text-editor\" data-id=\"0fcf170\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ol><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Social information-based methods tend to outperform non-social ones, suggesting social information enhances recommendation performance.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">MVBN&#8217;s substantial improvement over existing deep learning-based Social Recommendation models is credited to the custom embedding layer, BERT and sequence header capturing users&#8217; dynamic interest. The self-attention mechanism with BERT, multi-task learning, and consideration of bi-directional context of user-item interaction play significant roles in this improvement.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">When compared to sequential recommendation baselines like BERT4Rec and SASRec, MVBN still outperforms due to our embedding layer and multi-tasking. This validates our choice of the BERT network.<\/span><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fa43ee6 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-image\" data-id=\"fa43ee6\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"693\" height=\"365\" src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/08\/blog-3.png\" class=\"attachment-full size-full wp-image-12610\" alt=\"\" srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/08\/blog-3.png 693w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/08\/blog-3-300x158.png 300w\" sizes=\"(max-width: 693px) 100vw, 693px\" style=\"width:100%;height:52.67%;max-width:693px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8af8320 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"8af8320\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Fig: Visual comparison in terms of anomaly score curves on sample video for Violence Detection task.\u00a0 Here, yellow regions are the temporal ground-truths.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b204e23 elementor-widget elementor-widget-text-editor\" data-id=\"b204e23\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><b>Conclusion<\/b><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-df93121 elementor-widget elementor-widget-text-editor\" data-id=\"df93121\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tWe introduced the novel Multi-View BERT Network (MVBN) for Social Recommendation. MVBN uses neighbourhood sampling and sequence headers with BERT for a better representation of user-item interactions. It considers bidirectional contexts in predictions, and improves performance by incorporating users&#8217; social links in multi-task learning. Our model consistently surpasses the current top social recommendation algorithms. Future work will explore session-based interactions and social links with transformers.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0362925 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"0362925\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>To know more about Sony Research India\u2019s Research Publications, visit the \u2018Publications\u2019 section on our \u2018Open Innovation\u2019s page: <a href=\"https:\/\/www.sonyresearchindia.com\/open-innovation\/\" target=\"_blank\" rel=\"noopener\">Open Innovation with Sony R&amp;D \u2013 Sony Research India<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c0518a1 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c0518a1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-b15be70\" data-id=\"b15be70\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-55dd72b\" data-id=\"55dd72b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e06d72d elementor-widget elementor-widget-image\" data-id=\"e06d72d\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"322\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2.png\" class=\"attachment-full size-full wp-image-11786 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2.png 512w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2-300x189.png 300w\" data-sizes=\"(max-width: 512px) 100vw, 512px\" style=\"--smush-placeholder-width: 512px; --smush-placeholder-aspect-ratio: 512\/322;width:100%;height:62.89%;max-width:512px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-fd52b32\" data-id=\"fd52b32\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9b69060 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9b69060\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cfbe302\" data-id=\"cfbe302\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6d045fb elementor-widget elementor-widget-text-editor\" data-id=\"6d045fb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe introduced modules and techniques help the proposed method to align known class\nrepresentations effectively so that it can detect the unknown objects accurately. To validate\nthis, we carried out extensive experiments &#038; ablation studies and found that the proposed\nmethod outperforms existing SOTA methods with significant improvement on the MS-COCO\n&#038; PASCAL VOC dataset for the OSOD task.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f97c4c4 elementor-widget elementor-widget-text-editor\" data-id=\"f97c4c4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTo know more about the paper, visit: <a href=\"https:\/\/openaccess.thecvf.com\/content\/WACV2024\/papers\/Sarkar_Open-Set_Object_Detection_by_Aligning_Known_Class_Representations_WACV_2024_paper.pdf\" target=\"_blank\" rel=\"noopener\">Open-Set Object Detection by Aligning Known Class\nRepresentations (thecvf.com)<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9e2f9cc elementor-widget elementor-widget-text-editor\" data-id=\"9e2f9cc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTo know more about Sony Research India\u2019s Research Publications, visit the \u2018Publications\u2019\nsection on our \u2018Open Innovation\u2019s page: <a href=\"https:\/\/www.sonyresearchindia.com\/open-innovation\/\" target=\"_blank\" rel=\"noopener\">Open Innovation with Sony R&amp;D \u2013 Sony Research\nIndia<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Tushar Prakash summarises paper titled Enhancing Social Recommendation&#8230;<\/p>\n","protected":false},"author":1,"featured_media":12758,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[22,17],"tags":[],"class_list":["post-12723","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all-blogs","category-technology","entry"],"yoast_head":"\n<title>Summarizing \u2018Enhancing Social Recommendation with Multi-View BERT Network (MVBN)\u2019 - Sony Research India<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-enhancing-social-recommendation-with-multi-view-bert-network-mvbn\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" 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