Abstract
Collaborative filtering (CF) recommendation systems are one of the most popular and successful methods for recommending products to people. CF systems work by finding similarities between different people according to their past purchases, and using these similarities to suggest possible items of interest. In this work we show that CF systems can be enhanced using Internet browsing data and search engine query logs, both of which represent a rich profile of individuals' interests.
Original language | English |
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Title of host publication | 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1230-1238 |
Number of pages | 9 |
ISBN (Electronic) | 9781509020195 |
DOIs | |
State | Published - 22 Jun 2016 |
Externally published | Yes |
Event | 32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland Duration: 16 May 2016 → 20 May 2016 |
Publication series
Name | 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 |
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Conference
Conference | 32nd IEEE International Conference on Data Engineering, ICDE 2016 |
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Country/Territory | Finland |
City | Helsinki |
Period | 16/05/16 → 20/05/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.