Open AccessResearch on precision marketing of banking based on improved collaborative filtering algorithms
Author(s) -
Yuning Bian,
Yeli Li,
Qingtao Zeng,
Yanxiong Sun,
Linxuan Yu,
Wei He
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1449/1/012105
Subject(s) - collaborative filtering , context (archaeology) , variety (cybernetics) , the internet , computer science , banking industry , similarity (geometry) , algorithm , recommender system , data mining , marketing , business , world wide web , image (mathematics) , artificial intelligence , finance , paleontology , biology
With the rapid development of the Internet, the banking industry is facing great challenges. How to attract more customers under the huge impact of the Internet is the most important issue for banks to consider. In the context of big data, banks are using a variety of means to collect data and build their own customer portraits to adjust their marketing strategies. This article provides an improved collaborative filtering algorithm --- time-related similarity calculation method. This improved collaborative filtering algorithm can provide a new reference for the bank’s precision marketing.