یک مدل عصبی خودتوجه آگاه به موقعیت برای توصیه مبتنی بر جلسه شخصی سازی شده

نوع مقاله : علمی-پژوهشی

نویسندگان

Department of Computer Engineering, Yazd University, Yazd, Iran

چکیده

سیستم‌های توصیه مبتنی‌بر‌جلسه شخصی‌سازی‌شده، کلیک یا تعامل بعدی کاربر را بر اساس تعاملات قبلی کاربر در جلسه فعلی و جلسات تاریخی پیش‌بینی می‌کنند. مطالعات اخیر، بر روی شبکه‌های خودتوجه (SAN) برای بدست آوردن علایق کلی کاربران متمرکز شده‌اند. شبکه‌های خودتوجه با مدل کردن وابستگی‌های کلی بین تعاملات جلسه، توانایی بالایی در توصیه مبتنی‌بر‌جلسه، در مقایسه با دیگر رویکردهای شبکه‌های عمیق از خود نشان داده‌اند. اما این شبکه‌ها موقعیت و ترتیب اقلام در جلسه را در نظر نمی‌گیرند. درحالی‌که اطلاعات متوالی اقلام جلسه می‌تواند علایق ترتیبی کاربران را منعکس کند. در این مقاله، یک مدل عصبی خودتوجه آگاه‌به‌موقعیت (PASAN) برای توصیه مبتنی-بر‌جلسه شخصی‌سازی‌شده پیشنهاد می‌شود. این رویکرد، به منظور درنظرگرفتن ترتیب توالی جلسات، از یک مکانیزم رمزگذاری موقعیت معکوس برای اختصاص دادن یک تعبیه موقعیت به اقلام، مبتنی بر ترتیب آن‌ها در جلسه استفاده می‌کند. PASAN به طور مشترک از طریق شبکه خودتوجه، علایق کلی و از طریق رمزگذاری موقعیتی، علایق ترتیبی را یاد می‌گیرد. علاوه بر این، PASAN پیشنهادی علاوه بر جلسه فعلی از جلسات تاریخی کاربر هم استفاده و ترجیحات بلندمدت کاربران را مدل می‌کند. ابتدا PASAN مبتنی بر جلسات ناشناس آموزش داده می‌شود و سپس برای هر کاربر از طریق ترکیب وزنی جلسه فعلی و جلسات تاریخی کاربر، توصیه‌های شخصی‌سازی‌شده فراهم می‌شود. آزمایش‌های انجام شده بر روی دو مجموعه داده واقعی نشان می‌دهد مدل پیشنهادی در مقایسه با سایر روش‌ها بهتر عمل می‌کند. مدل پیشنهادی بر روی مجموعه داده Reddit، از نظر دقت حدود 20 درصد و از نظر میانگین رتبه متقابل حدود 8 درصد بهبود یافته است.

کلیدواژه‌ها

عنوان مقاله [English]

A Position-Aware Self-Attention Neural Model for Personalized Session-Based Recommendation

نویسندگان [English]

  • Azam Ramazani
  • Ali-Mohammad Zareh-Bidoki
  • Mohammad-Reza Pajoohan
Department of Computer Engineering, Yazd University, Yazd, Iran
چکیده [English]

Personalized session-based recommendation seeks to predict the user's next click or interaction based on the user's previous interactions and behaviour in the present and historical sessions. Recent research has focused on self-attention networks (SANs) to capture users' global interests and behaviours. Thanks to their modeling of the global dependencies among session interactions, SANs have exhibited superior performance in session-based recommendation compared to other deep network approaches. These networks do not account for items' position and order in the session sequence. Nonetheless, the sequential data of the sessions and the item order in the session sequence can reflect the user's sequential interests and improve the recommendation's performance. This article proposes a Position-Aware Self-Attention Neural (PASAN) model for personalized session-based recommendations. The model uses a reverse-position encoding mechanism that assigns position embedding to items based on their order in the session sequence. The PASAN model jointly learns global interests through the self-attention network and sequential interests via positional encoding. Moreover, it employs users' historical and current session interactions to model their long-term preferences. First, PASAN is trained using anonymous sessions. Subsequently, personalized recommendations are provided for individual users by combining the current session with their historical sessions in a weighted manner. Experiments conducted on two real-world datasets show that the proposed model outperforms the current methods. The proposed model has improved by about 20% in terms of precision and about 8% in terms of mean reciprocal rank on the Reddit dataset.

کلیدواژه‌ها [English]

  • Session-based recommendation
  • personalized recommendation
  • self-attention networks
  • deep learning

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