ترجمه فارسی مقاله مدلسازی و تجزیه و تحلیل تأثیر صفحات غیر موردی بر پیش بینی متوالی مورد بعدی

• کتاب صدها نکته فارسی (خودمونی) – نسخه PDF — زبان ساده و کاربردی
  مشاهده نمونه نسخه نکات ساده
• کتاب صدها نکته رسمی فارسی – نسخه PDF — نگارش استاندارد و علمی
  مشاهده نمونه نسخه نکات رسمی
• کتاب صدها پرسش و پاسخ تشریحی – نسخه PDF
  — هر سؤال همراه با پاسخ کامل برای درک عمیق مفاهیم
  مشاهده نمونه نسخه پرسش و پاسخ
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه یادگیری سریع
  — پاسخ‌ها بلافاصله بعد از سؤال برای مرور سریع
  مشاهده نمونه نسخه کوییز سریع
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه خودآزمایی
  — پاسخ‌ها در انتهای بخش‌ها برای سنجش واقعی یادگیری
  مشاهده نمونه نسخه آزمونی

🎯 این بسته یک دورهٔ آموزشی کامل و چندلایه است؛ شامل ویدیوهای آموزشی، کتاب‌ها، تمرین‌ها و خودآزمایی.

چکیده

Analyzing the sequence of historical interactions between users and items, sequential recommendation models learn user intent and make predictions about the next item of interest. Next to these item interactions, most systems also have interactions with pages not related to specific items, for example navigation pages, account pages, and pages for a specific category, which may provide additional insights into the user's interests. However, while there are several approaches to integrate additional information about items and users, the topic of integrating non-item pages has been less explored. We use the hypotheses testing framework HypTrails to show that there is indeed a relationship between these non-item pages and the items of interest and fill this gap by proposing various approaches of representing non-item pages (e.g, based on their content) to use them as an additional information source for the task of sequential next-item prediction. We create a synthetic dataset with non-item pages highly related to the subsequent item to show that the models are generally capable of learning from these interactions, and subsequently evaluate the improvements gained by including non-item pages in two real-world datasets. We adapt eight popular sequential recommender models, covering CNN-, RNN- and transformer-based architectures, to integrate non-item pages and investigate the capabilities of these models to leverage their information for next item prediction. We also analyze their behavior on noisy data and compare different item representation strategies. Our results show that non-item pages are a valuable source of information, but representing such a page well is the key to successfully leverage them. The inclusion of non-item pages can increase the performance for next-item prediction in all examined model architectures with a varying degree.

چکیده به فارسی (ترجمه ماشینی)