Identifying meaningful recommendation partners in collaborative filtering framework for improved recommendations

Please use this identifier to cite or link to this item: http://idr.iimranchi.ac.in:8080/xmlui/handle/123456789/556

Full metadata record

DC Field	Value	Language
dc.contributor.author	Kumar, Rahul.	-
dc.date.accessioned	2019-07-30T10:43:41Z	-
dc.date.available	2019-07-30T10:43:41Z	-
dc.date.issued	2017-11-04	-
dc.identifier.uri	http://idr.iimranchi.ac.in:8080/xmlui/handle/123456789/556	-
dc.description.abstract	Internet today has surrounded the users with its symbolic problem of information overload where a consumer has far more than the required choices. In the realm of e-commerce industry, recommender systems (RS) utilize the information filtering technology to proactively handle 'information overload', the ubiquitous problem in cyberspace.	en_US
dc.language.iso	en	en_US
dc.publisher	Indian Institute of Management Ranchi	en_US
dc.subject	E-commerce	en_US
dc.subject	Recommender Systems	en_US
dc.subject	Collaborative filtering	en_US
dc.subject	Recommendation Partners	en_US
dc.subject	IIM Ranchi	en_US
dc.title	Identifying meaningful recommendation partners in collaborative filtering framework for improved recommendations	en_US
dc.type	Thesis	en_US
dc.guide	Bala, Pradip Kumar	-
Appears in Collections:	Thesis

Files in This Item:

There are no files associated with this item.