Abstract
Making accurate recommendations with the plethora of information has become a challenging task for researchers indulge in the area of recommender system. The most widely adopted collaborative filtering recommendation technique provides more desirable and appropriate recommendations to satisfy the end user. However, sparsity and scalability of collaborative filtering recommendation technique are still under consideration. In this work, a novel extreme learning machine-based collaborative filtering model is designed and developed to handle the issues of sparsity and scalability. To deal with the problem of sparsity and scalability, users’ rating dataset is transformed into a dense and compressed dataset by measuring users’ interestingness in items based on different features. Further, the extreme learning machine is exploited on the transformed dataset for predicting the items’ ratings. Experimental outcomes are analyzed in comparison with the state-of-the-art recommendation approaches. The superiority of results of the proposed technique reveals that the ELM-based CF model outperforms the other comparative techniques.
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Tyagi, S., Yadav, P., Arora, M., Vashisth, P. (2019). Predicting Users’ Interest Through ELM-Based Collaborative Filtering. In: Shukla, R.K., Agrawal, J., Sharma, S., Singh Tomer, G. (eds) Data, Engineering and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-6347-4_3
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DOI: https://doi.org/10.1007/978-981-13-6347-4_3
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