Demand for personalized recommendation systems elevated recently by e-commerce, news portals etc., to grab the customer interest on the sites. Collaborative filtering proves to be powerful technique but it always suffers from data sparsity, cold-start and robustness issues. These issues have been tackled by some approaches resulting in higher accuracy. Few of them take user profiles, item attributes and rating time as the side information along with ratings to give interpretative personalized recommendations. These type of approaches tries to find which factors mainly impacted the user to rate an item. Another approach extends the single-criteria ratings of collaborative filtering to multi-criteria ratings. Our approach exploits non-linear interpretative recommendations by exploring Multi-criteria ratings by combination of Autoencoders with dropout layer and firefly algorithm optimized weights for deep neural networks. Our approach solves data sparsity, scalability issues and fetch accurate recommendations. Experimental evaluations have been done using Yahoo! Movie and MovieLens datasets. Our approach outperforms in robustness and accuracy with respect to previous research works.

Recommendation Systems finds the user preferences based on the purchase history of an individual using data mining and machine learning techniques. To reduce the time taken for computation Recommendation systems generally use a pre-processing technique which in turn helps to increase high low performance and over comes over-fitting of data. In this paper, we propose a hybrid collaborative filtering algorithm using firefly and agglomerative hierarchical clustering technique with priority queue and Principle Component Analysis (PCA). We applied our hybrid algorithm on movielens dataset and used Pearson Correlation to obtain Top N recommendations. Experimental results show that the our algorithm delivers accurate and reliable recommendations showing high performance when compared with  existing algorithms.