![](/images/graphics-bg.png)
Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation
المؤلفون المشاركون
Li, Zhao
Zhang, Long
Lei, Chenyi
Chen, Xia
Gao, Jianliang
Gao, Jun
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-13
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Modeling user behaviors as sequential learning provides key advantages in predicting future user actions, such as predicting the next product to purchase or the next song to listen to, for the purpose of personalized search and recommendation.
Traditional methods for modeling sequential user behaviors usually depend on the premise of Markov processes, while recently recurrent neural networks (RNNs) have been adopted to leverage their power in modeling sequences.
In this paper, we propose integrating attention mechanism into RNNs for better modeling sequential user behaviors.
Specifically, we design a network featuring Attention with Long-term Interval-based Gated Recurrent Units (ALI-GRU) to model temporal sequences of user actions.
Compared to previous works, our network can exploit the information of temporal dimension extracted by time interval-based GRU in addition to normal GRU to encoding user actions and has a specially designed matrix-form attention function to characterize both long-term preferences and short-term intents of users, while the attention-weighted features are finally decoded to predict the next user action.
We have performed experiments on two well-known public datasets as well as a huge dataset built from real-world data of one of the largest online shopping websites.
Experimental results show that the proposed ALI-GRU achieves significant improvement compared to state-of-the-art RNN-based methods.
ALI-GRU is also adopted in a real-world application and results of the online A/B test further demonstrate its practical value.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Zhao& Zhang, Long& Lei, Chenyi& Chen, Xia& Gao, Jianliang& Gao, Jun. 2020. Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1142740
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Zhao…[et al.]. Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1142740
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Zhao& Zhang, Long& Lei, Chenyi& Chen, Xia& Gao, Jianliang& Gao, Jun. Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1142740
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142740
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)