{"id":12925,"date":"2024-11-19T06:49:45","date_gmt":"2024-11-19T06:49:45","guid":{"rendered":"https:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-enhancing-social-recommendation-with-multi-view-bert-network-mvbn-copy\/"},"modified":"2024-11-19T10:22:06","modified_gmt":"2024-11-19T10:22:06","slug":"summarizing-llm-brec-personalizing-session-based-social-recommendation-with-llm-bert-fusion-framework","status":"publish","type":"post","link":"https:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-llm-brec-personalizing-session-based-social-recommendation-with-llm-bert-fusion-framework\/","title":{"rendered":"Summarizing \u2018LLM-BRec: Personalizing Session-based Social Recommendation with LLM-BERT Fusion Framework\u2019"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"12925\" class=\"elementor elementor-12925\" 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 \u2018LLM-BRec: Personalizing Session-based Social Recommendation with LLM-BERT Fusion Framework\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-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile 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\/Overview-of-proposed-framework-qx9ycevn19n7q93oajt33kq9ru0n0n8z6i46ickld8.png\" title=\"Overview of proposed framework\" alt=\"Overview of proposed framework\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" class=\"lazyload\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/542;\" \/>\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<p>Tushar Prakash summarises paper titled \u201c<a href=\"https:\/\/openreview.net\/pdf?id=gwHVlTNKsG\" target=\"_blank\" rel=\"noopener\">LLM-BRec: Personalizing Session-based Social Recommendation with LLM-BERT Fusion Framework<\/a>\u201d co-authored by Raksha Jalan and Niranjan Pedanekar. Accepted at <a href=\"https:\/\/coda.io\/@sigir\/gen-ir-24\" target=\"_blank\" rel=\"noopener\">2nd Workshop on Gen-IR at 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)<\/a><\/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-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>The rapid expansion of e-commerce and online entertainment has led to increased user expectations for personalized recommendations, driving the development of recommendation systems that effectively filter irrelevant information. Session-based Recommendation (SR) techniques focus on understanding user preferences within a temporal context to enhance prediction accuracy. Initial efforts in SR, such as Anonymous Session-based Recommendations (ASR), target scenarios where user IDs are not available, while Personalized Session-based Recommendations (PSR) utilize user IDs for improved cross-session information transfer. As social media gains traction, integrating social relationships into recommendation systems has become crucial, leading to the rise of Session-based Social Recommendations (SSR). Although SSR has shown promise through approaches like DGRec and SERec, they have limitations in fully leveraging personalized user information and tend to rely on computationally intensive algorithms.<br \/><br \/>To address these challenges, the proposed \u201cLLM-BRec\u201d framework introduces a Social-aware Heterogeneous Graph (SHG) for enhanced user and item representation and utilizes BERT for efficient session modelling. This framework builds a comprehensive knowledge graph from user interactions and social connections, allowing for better prediction of user interactions within sessions. By employing BERT\u2019s self-attention mechanism, LLM-BRec significantly reduces training time by 50% and inference time by 80% compared to state-of-the-art methods. Furthermore, it emphasizes the importance of post-training user profiling with Language Models (LLMs) to enhance recommendation performance while maintaining computational efficiency. The effectiveness of LLM-BRec is validated through performance comparisons on both social and non-social recommendation datasets, consistently outperforming existing state-of-the-art models.<\/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-a880aa7 elementor-widget elementor-widget-image\" data-id=\"a880aa7\" 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\/key-results-qx9yhor1dsv4vhfvhtvq38tdro0s7i6r8lw9g8r2ui.png\" title=\"key-results\" alt=\"key-results\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" class=\"lazyload\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/593;\" \/>\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-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\tBelow are the key points derived from Tables 1 and 2 that highlight the superior performance of LLM-BRec:\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>Model Performance:<\/b><span style=\"font-weight: 400;\"> LLM-BRec outperforms existing baselines (ASR, PSR, SSR) on both social and non-social datasets, showcasing its effectiveness in recommendation systems.<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>LLM-Based User Profiling:<\/b><span style=\"font-weight: 400;\"> The significant improvement over traditional models highlight the importance of incorporating LLM-based user profiling to better understand user preferences.\n<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Efficiency:<\/b><span style=\"font-weight: 400;\"> LLM-BRec surpasses recent PSR and SSR models that utilize computationally heavy attention and graph-based algorithms for session modelling, indicating the effectiveness of bidirectional context in user-item interactions.\n<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Social Network Utilization:<\/b><span style=\"font-weight: 400;\"> While state-of-the-art models like DGRec and SERec leverage social networks for improved user preferences, they do not exceed LLM-BRec\u2019s performance, demonstrating the power of LLM-based user profiling combined with SHG and BERT.\n<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Comparison with RNN and LSTM:<\/b><span style=\"font-weight: 400;\"> Experiments replacing BERT with RNN and LSTM show that BERT is more effective at creating rich representations of users\u2019 session-level behaviour sequences due to its bidirectional context learning.\n<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Non-Social SR Performance:<\/b><span style=\"font-weight: 400;\"> LLM-BRec significantly outperforms other models (e.g., BERT4Rec, GRU4Rec, HRNN) in non-social SR scenarios, emphasizing the importance of efficient embeddings and capturing long-term user interests.\n<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Overall Contribution:<\/b><span style=\"font-weight: 400;\"> The results demonstrate that the combination of SHG for embeddings, efficient session modelling with BERT, and LLM-based user profiling enhances the overall performance of LLM-BRec.\n<\/span><\/li>\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-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\tThis article presents LLM-BRec, a framework that enhances Session-based Social Recommendation (SSR) systems by utilizing LLMs for personalized user profiles, a Social-aware Heterogeneous Graph (SHG) for user and item representations, and BERT for session modelling. LLM-BRec improves recommendation accuracy while reducing computational costs, outperforming state-of-the-art methods across multiple datasets. Future research directions include integrating additional user context like temporal dynamics, exploring scalability in real-time systems, adapting the framework for various domains, incorporating multi-modal data sources, and enhancing user privacy in profiling.\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 fetchpriority=\"high\" decoding=\"async\" width=\"512\" height=\"322\" src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2.png\" class=\"attachment-full size-full wp-image-11786\" alt=\"\" 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\" sizes=\"(max-width: 512px) 100vw, 512px\" style=\"width:100%;height:62.89%;max-width:512px\" \/>\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 \u201cLLM-BRec: Personalizing&#8230;<\/p>\n","protected":false},"author":1,"featured_media":12947,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[22,17],"tags":[],"class_list":["post-12925","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all-blogs","category-technology","entry"],"yoast_head":"\n<title>Summarizing \u2018LLM-BRec: Personalizing Session-based Social Recommendation with LLM-BERT Fusion Framework\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-llm-brec-personalizing-session-based-social-recommendation-with-llm-bert-fusion-framework\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" 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