ترکیب تجزیه نامنفی ماتریسی با روابط اعتماد برای توصیه در شبکه‌های اجتماعی

نوع مقاله : علمی-پژوهشی

نویسندگان

1 گروه مهندسی کامپیوتر - دانشگاه کردستان

2 گروه ریاضی کاربردی - دانشگاه کردستان

چکیده

سیستم‌های توصیه‌گر، یکی از ابزارهای مؤثر برای کمک به کاربران است تا آیتم‌های مورد علاقه خودشان را پیدا کنند. سیستم‌های پالایش گروهی یکی از مشهورترین الگوریتم‌های توصیه به‌شمار می‌روند و در کارهای تجاری مختلفی استفاده شده‌اند. اما این سیستم‌ها در برخورد با کاربران و کالاهایی( آیتم‌هایی) که اطلاعات کمی از آن‌ها وجود دارد ( کاربران یا کالاهای با شروع سرد) دارند، کارایی ضعیفی از خود نشان می‌دهند. برای مقابله با این چالش، در این مقاله، یک روش جدید مبتنی بر اطلاعات شبکه اجتماعی کاربران ارائه می‌شود که اطلاعات اعتماد بین کاربران را با تجزیه نامنفی ماتریس ترکیب می‌کند تا یک مدل مناسب برای توصیه به کاربر ایجاد شود. روش پیشنهادی اطلاعات مهم مانند، رتبه و اعتماد را برای کاهش پراکندگی داده و برخورد با مشکلات ناشی از شروع سرد، استفاده می‌کند. به‌علاوه، در روش پیشنهادی از راه‌کار بهینه‌سازی جهت متناوب برای افزایش همگرایی الگوریتم و کاهش پیچیدگی زمانی به‌طور مناسبی استفاده می‌شود. برای ارزیابی روش پیشنهادی چندین آزمایش روی دو مجموعه داده معتبر و مشهور انجام شده است. نتایج تجربی نشان می‌دهد که روش پیشنهادی، به‌ویژه، برای کاربران شروع سرد عملکرد بهتری نسبت به روش‌های جدید، برای توصیه در شبکه‌های اجتماعی دارد.

کلیدواژه‌ها

عنوان مقاله [English]

Combining Nonnegative Matrix Factorization technique with Trust Relationships for Recommendation in Social Networks

نویسندگان [English]

  • H. Parvin 1
  • P. Moradi 1
  • Sh. Esmaeili 2
1 Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
2 Department of Applied Mathematics, University of Kurdistan, Sanandaj, Iran
چکیده [English]

Recommender systems has shown as effective tools that are proposed for helping users to select their interested items. Collaborative filtering is a well-known and frequently used recommender system applied successfully in many e-commerce websites. However, these systems have poor performance while facing cold-start users (items). To address such issues, in this paper, a social regularization method is proposed which combines the social network information of users in a nonnegative matrix factorization framework. The proposed method integrates multiple information sources such as user-item ratings and trust statements to reduce the cold-start and data sparsity issues. Moreover, the alternating direction method is used to improve the convergence speed and reduce the computational cost. We use two well-known datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation methods for recommendation in social networks.

کلیدواژه‌ها [English]

  • Recommender systems؛ nonnegative matrix factorization
  • trust relationships
  • alternating direction method
  • collaborative filtering
  • cold-start

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