Himanshu Budania

Atal Bihari Vajpayee Indian Institute of Information Technology and Management

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Pramod Kumar Singh

Atal Bihari Vajpayee Indian Institute of Information Technology and Management

Prachi Singh

Indian Institute of Science Bangalore

Anusha Gupta

Trinity College Dublin

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Atal Bihari Vajpayee Indian Institute of Information Technology and Management

Indian Institute of Science Bangalore

Trinity College Dublin

Manav Rachna International Institute of Research and Studies

Banaras Hindu University

Institute of Medical Sciences

Netaji Subhas University of Technology

Bharati Vidyapeeth Deemed University

Indian Institute of Information Technology Allahabad

Indian Agricultural Research Institute

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Person Versus Non-person Classification of Twitter Handle

Advances in intelligent systems and computing (2018)

Himanshu Budania Pramod Kumar Singh

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10.1007/978-3-319-76351-4_11

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Facebook Based Choice Filtering

Anusha Gupta Himanshu Budania Prachi Singh Pramod Kumar Singh

The rating and review based algorithms like collaborative filtering faces the cold start problem on addition of new items or new users in the recommender system because of lack of ratings and reviews. The primary aim of this work is to solve the cold start problem faced by rating based recommendation system and also increase the accuracy of recommender systems. We used the content-based filtering algorithm along with the cross-domain data taken from Facebook so that the problem of addition of a new user problem and a new item in recommender system can be solved. The combination of both content-based filtering and cross-domain data obtains better results than the collaborative filtering and shows a good way to deal with the cold start problem.

Cold start (automotive)

10.1109/iacc.2017.0179

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