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DIM: Adaptively Combining User Interests Mined at Different Stages Based on Deformable Interest Model
المؤلفون المشاركون
Wang, Xiaoru
Li, Yueli
Yu, Zhihong
Li, Fu
Zhang, Heng
Cai, Yali
Li, Lixian
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-30
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
User interest mining is widely used in the fields of personalized search and personalized recommendation.
Traditional methods ignore the formation of user interest which is a process that evolves over time.
This leads to the inability to accurately describe the distribution of user interest.
In this paper, we propose the interest tracking model (ITM).
To add the timing, ITM uses Dirichlet distribution and multinomial distribution to describe the evolutional process of interest topics and frequent patterns, which well adapts to the evolution of user interest hidden in short texts between different time slices.
In addition, it is well known that user interest is composed of long-term interest and situational interest including short-term interest and social hot topics.
State-of-the-art methods simply regard the users’ long-term interest as the users’ final interest, which makes those unable to completely describe the user interest distribution.
To solve this problem, we propose the deformable interest model (DIM) which designs an objective function to combine users’ long-term interest and situational interest and more comprehensively and accurately mine user interest.
Furthermore, we present the degree of deformation which measures the subinterest's degree of influence on final interest and propose in DIM the influence real-time update mechanism.
The mechanism adaptively updates the degree of deformation through the linear iteration and reduces the degree of dependence of the interest model on training sets.
We present results via a dataset consisting of Flickr users and their uploaded information in three months, a dataset consisting of Twitter users and their tweets in three months, and a dataset consisting of Instagram users and their uploaded information in three months, showing that the perplexity is reduced to 0.378, the average accuracy is increased to 94%, and the average NMI is increased to 0.20, which prove better interest prediction.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Xiaoru& Li, Yueli& Yu, Zhihong& Li, Fu& Zhang, Heng& Cai, Yali…[et al.]. 2020. DIM: Adaptively Combining User Interests Mined at Different Stages Based on Deformable Interest Model. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1195170
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Xiaoru…[et al.]. DIM: Adaptively Combining User Interests Mined at Different Stages Based on Deformable Interest Model. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1195170
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Xiaoru& Li, Yueli& Yu, Zhihong& Li, Fu& Zhang, Heng& Cai, Yali…[et al.]. DIM: Adaptively Combining User Interests Mined at Different Stages Based on Deformable Interest Model. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1195170
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1195170
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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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
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