• Er.Meenakshi Sharma
• Dr.Satpal

There are number of online services and size of users have increased dramatically over the past years. In long-term web services are important to increase the web API economy, how to recommend the long-tail web services efficiently is a primary issue. Moreover, to focus on this problem, the traditional web based recommendation services performs poorly on long-tail side. To overcome the problem of severe sparsity of historical usage data and unsatisfactory quality of description content, we are focusing on Convolutional Neural Network approach to boost the performance of the network and reduce time consumption. Recommendation engine collects and saves suggestions from individuals who know about the decisions that they confronted and furthermore it values their points of view and identifies them as the specialists. Two ordinary entities which seem in any recommendation engine are the products and individuals. A DNN involved convolutional layer exchange with maxpooling layer pursued by completely associated layers and a last classification layer .

CNN, Recommendation system, Long -Tail. Sparse Data.

"Hybrid Deep Learning Long Tail Services Recommendation System:An approach to solve the problem of long tail services ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.205-215, May 2019, Available :http://www.jetir.org/papers/JETIRCY06032.pdf

"Hybrid Deep Learning Long Tail Services Recommendation System:An approach to solve the problem of long tail services ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp205-215, May 2019, Available at : http://www.jetir.org/papers/JETIRCY06032.pdf