سیستم توصیه‌گر مبتنی بر جلسه با استفاده از روش ترکیبی شبکه‌های عصبی بازگشتی و گراف

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه کامپیوتر، دانشکده مهندسی، دانشگاه زنجان، ایران

2 استادیار گروه کامپیوتر، دانشکده مهندسی، دانشگاه زنجان، ایران

3 استادیار، دانشکده فناوری اطلاعات، دانشگاه صنعتی کرمانشاه، کرمانشاه، ایران

چکیده

سیستم‌های توصیه‌گر مبتنی بر جلسه نوعی از سیستم‌های توصیه‌گر هستند که با مدل‌سازی وابستگی‌های بین تعاملات کاربر و آیتم‌ها، به دنبال معرفی مناسب‌ترین آیتم یا مجموعه‌ای از آیتم‌ها به کاربر هستند. موضوع مهم در عملکرد این سیستم‌ها، توانایی کشف وابستگی‌های مختلف در یک جلسه است. درحالی‌که مدل‌های موجود اغلب تنها بر یکی از این دو نوع وابستگی ترتیبی یا پیچیده تمرکز دارند، در این پژوهش یک مدل جدید ارائه‌شده است که از شبکه عصبی گراف برای شناسایی وابستگی‌های پیچیده و غیرترتیبی بین آیتم‌ها، و از شبکه عصبی بازگشتی برای مدل‌سازی وابستگی‌های ترتیبی درون جلسه استفاده می‌کند. این ترکیب به مدل امکان می‌دهد تا درکی غنی‌تر و دقیق‌تر از هدف کاربر به دست آورده و در نتیجه، نسبت به روش‌های متعارف که تنها به یکی از این جنبه‌ها می‌پردازند، دقت پیش‌بینی بالاتری را ارائه دهد. ارزیابی جامع بر روی دو مجموعه داده پرکاربرد Yoochoose و Diginetica، برتری روش معرفی‌شده را در مقایسه با جدیدترین روش‌های موجود به اثبات می‌رساند. نتایج نشان می‌دهد که RGNF-SRec به ترتیب به بهبود 40/0٪ در معیار P@20 و 07/1٪ در معیار MRR@20 نسبت به قوی‌ترین مدل پایه بر روی این مجموعه داده‌ها دست‌یافته و توانایی خود را در ارائه توصیه‌های دقیق‌تر برای آیتم بعدی به نمایش می‌گذارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Session-based recommender system integrating analysis of sequential and complex dependencies using recurrent neural networks and graphs

نویسندگان [English]

  • Meysam Mirzaee 1
  • Hossein Mohammadi 2
  • Sajad Ahmadian 3
1 PhD Student, Faculty of Computer, Zanjan University, Zanjan, Iran
2 Assistant Professor, Faculty of Computer, Zanjan University, Zanjan, Iran
3 Assistant Professor, Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
چکیده [English]

Session-based recommender systems are a type of recommender systems that model the dependencies between items in user interactions to find the most appropriate item or set of items to the user. An important issue in the performance of these systems is the ability to discover different dependencies in a session. While existing models often focus on only one of the two dependency types—either sequential or complex—this study proposes a novel model that integrates both. Specifically, a Graph Neural Network (GNN) is employed to identify complex and non-sequential dependencies among items, while a Recurrent Neural Network (RNN) is utilized to model the sequential dependencies within sessions. This hybrid architecture enables the model to obtain a more accurate representation of user intent, thereby achieving higher prediction accuracy compared to conventional approaches that address only one dependency type. Comprehensive evaluations conducted on two widely used benchmark datasets, Yoochoose and Diginetica, demonstrate the superiority of the proposed method over state-of-the-art baselines. The results show that RGNF-SRec achieves improvements of 0.40% in P@20 and 1.07% in MRR@20 compared to the strongest baseline model on these datasets, highlighting its enhanced capability in providing more accurate next-item recommendations.

کلیدواژه‌ها [English]

  • Session-based recommender systems
  • Graph
  • Recurrent Neural Networks

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پژوهش های نظری و کاربردی هوش ماشینی
دوره 3، شماره 1
شهریور 1404
صفحه 193-211

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