Enhanced Service Recommender and Ranking System Using Browsing Patterns of Users

被引：0
|
作者：
Gudla, Suresh Kumar [1 ]
Bose, Joy [1 ]
Sane, Koushik Reddy [1 ]
机构：
[1] Samsung R&D Inst, Bangalore, Karnataka, India
来源：
2019 16TH IEEE ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC) | 2019年
关键词：
web services; user modeling; clustering algorithms;
D O I：
10.1109/ccnc.2019.8651758
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
We present an enhanced service recommender system for web services, which takes existing recommendations from services or content providers and based on topics identified by the user's recent browsing history (including past clicks and searches) re-ranks the recommended URLs for each service, giving an aggregate re-ranked recommendation list. This information can be used by third party services for giving more relevant recommendations and notifications to the user, as well as to build a user interests profile. We have implemented our system using a modified version of LDA to cluster the browsing history, and validated it using browsing data gathered from a selection of users.
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页数：6
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