A study and analysis of recommendation systems for location-based social network (LBSN) with big data Narayanan, Murale
Material type:
TextPublication details: Bangalore Indian Institute of Management - Bangalore 22 January 2016Description: 25-30Subject(s):
In:
RAVI aNSHUMAN V. IIMB Management Review
| Item type | Current library | Call number | Vol info | Status | Notes | Date due | Barcode | Item holds | |
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Journal Article
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Main Library | Vol. 28, No. 1/5555646JA4 (Browse shelf(Opens below)) | Available | 5555646JA4 | |||||
Journals and Periodicals
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Main Library On Display | JRNL/GEN/Vol 28, Issue 1/5555646 (Browse shelf(Opens below)) | Vol 28, Issue 1 (30/04/2015) | Not for loan | March, 2016 | 5555646 |
Recommender systems play an important role in our day-to-day life. A recommender system automatically suggests an item to a user that he/she might be interested i. Small-scale datasets are used to provide recommendations based on location, but in real time, the volume of data is large. We have selected Foursquare dataset to study the need for big data in recommendation systems for location-based social network (LBSN). A few quality parameters like parallel processing and multimodel interface have been selected to study the need for big data in recommender systems. This paper provides a study and analysis of quality parameters of recommendation systems for LBSN with big data.
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