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.
引用
收藏
页数：6
相关论文
共 50 条
  • [21] Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
    Tang, Jiaxi
    Wang, Ke
    KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2018, : 2289 - 2298
  • [22] An intelligent recommender system using sequential web access patterns
    Zhou, BY
    Hui, SC
    Chang, KY
    2004 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2, 2004, : 393 - 398
  • [23] Text recommender system using user's usage patterns
    Kim, Yong Soo
    INDUSTRIAL MANAGEMENT & DATA SYSTEMS, 2011, 111 (1-2) : 282 - 297
  • [24] A Pipelined Hybrid Recommender System for Ranking the Items on the Display
    Oh, Jaehoon
    Kim, Sangmook
    Yun, Se-Young
    Choi, Seungwoo
    Yi, Mun Yong
    PROCEEDINGS OF THE WORKSHOP ON ACM RECOMMENDER SYSTEMS CHALLENGE (RECSYS CHALLENGE 2019), 2019,
  • [25] Embedding ranking-oriented recommender system graphs
    Hekmatfar, Taher
    Haratizadeh, Saman
    Goliaei, Sama
    EXPERT SYSTEMS WITH APPLICATIONS, 2021, 181
  • [26] Increasing Serendipity of Recommender System with Ranking Topic Model
    Xiao, Zhibo
    Che, Feng
    Miao, Enuo
    Lu, Mingyu
    APPLIED MATHEMATICS & INFORMATION SCIENCES, 2014, 8 (04): : 2041 - 2053
  • [27] CARES: A Ranking-Oriented CADAL Recommender System
    Yang, Chenxing
    Wei, Baogang
    Wu, Jiangqin
    Zhang, Yin
    Zhang, Liang
    JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, 2009, : 203 - 211
  • [28] Hybrid recommender system with core users selection
    Chenxia Jin
    Jusheng Mi
    Fachao Li
    Jiahuan Zhang
    Soft Computing, 2022, 26 : 13925 - 13939
  • [29] Guide and Retain Users: Interactive Recommender System
    Min, Zhang
    PROCEEDINGS OF 2018 10TH INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2018), 2018, : 44 - 48
  • [30] Hybrid recommender system with core users selection
    Jin, Chenxia
    Mi, Jusheng
    Li, Fachao
    Zhang, Jiahuan
    SOFT COMPUTING, 2022, 26 (24) : 13925 - 13939
12345