Multi-criteria– Recommendations using Autoencoder and Deep Neural Networks with Weight Optimization using Firefly Algorithm

Document Type : Original Article


Department of CSE, NIT Warangal, Warangal, India


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.


Main Subjects

  1. Tayefeh Mahmoudi, M., Badie, K., Moosaee, M. and Souri, A., "A compositional adaptation-based approach for recommending learning resources in software development", International Journal of Engineering, Transactions A: Basics, Vol. 35, No. 7, (2022), 1317-1329.
  2. Lops, P., Gemmis, M.d. and Semeraro, G., "Content-based recommender systems: State of the art and trends", Recommender Systems Handbook, (2011), 73-105.
  3. Hofmann, T. and Puzicha, J., "Latent class models for collaborative filtering", in IJCAI. Vol. 99, (1999).
  4. Linden, G., Smith, B. and York, J., "Amazon. Com recommendations: Item-to-item collaborative filtering", IEEE Internet Computing, Vol. 7, No. 1, (2003), 76-80. MIC. 2003.1167344
  5. Ferreira, D., Silva, S., Abelha, A. and Machado, J., "Recommendation system using autoencoders", Applied Sciences, Vol. 10, No. 16, (2020), 5510.
  6. Konstan, J.A., Riedl, J., Borchers, A. and Herlocker, J.L., "Recommender systems: A grouplens perspective", in Recommender Systems: Papers from the 1998 Workshop (AAAI Technical Report WS-98-08), AAAI Press Menlo Park., (1998), 60-64.
  7. Barzegar Nozari, R., Koohi, H. and Mahmodi, E., "A novel trust computation method based on user ratings to improve the recommendation", International Journal of Engineering, Transactions C: Aspects, Vol. 33, No. 3, (2020), 377-386.
  8. Adomavicius, G. and Tuzhilin, A., "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions", IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 6, (2005), 734-749.
  9. Vozalis, M.G. and Margaritis, K.G., "A recommender system using principal component analysis", in Published in 11th panhellenic conference in informatics, Citeseer. (2007), 271-283.
  10. Koren, Y., Bell, R. and Volinsky, C., "Matrix factorization techniques for recommender systems", Computer, Vol. 42, No. 8, (2009), 30-37.
  11. Sedhain, S., Menon, A.K., Sanner, S. and Xie, L., "Autorec: Autoencoders meet collaborative filtering", in Proceedings of the 24th international conference on World Wide Web. (2015), 111-112.
  12. Liang, D., Krishnan, R.G., Hoffman, M.D. and Jebara, T., "Variational autoencoders for collaborative filtering", in Proceedings of the 2018 world wide web conference., (2018), 689-698.
  13. Vlachos, M., Vassiliadis, V.G., Heckel, R. and Labbi, A., "Toward interpretable predictive models in b2b recommender systems", IBM Journal of Research and Development, Vol. 60, No. 5/6, (2016),
  14. Chen, W.-H., Hsu, C.-C., Lai, Y.-A., Liu, V., Yeh, M.-Y. and Lin, S.-D., "Attribute-aware recommender system based on collaborative filtering: Survey and classification", Frontiers in big Data, Vol. 2, (2020), 49.
  15. Harper, F.M. and Konstan, J.A., "The movielens datasets: History and context", Acm Transactions on Interactive Intelligent Systems (tiis), Vol. 5, No. 4, (2015), 1-19.
  16. Su, Y., Erfani, S.M. and Zhang, R., "Mmf: Attribute interpretable collaborative filtering", in 2019 International Joint Conference on Neural Networks (IJCNN), IEEE., (2019), 1-8.
  17. Yücebaş, S.C., "Movieann: A hybrid approach to movie recommender systems using multi layer artificial neural networks", Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Vol. 5, No. 2, (2019), 214-232.
  18. Tallapally, D., Sreepada, R.S., Patra, B.K. and Babu, K.S., "User preference learning in multi-criteria recommendations using stacked auto encoders", in Proceedings of the 12th ACM conference on recommender systems., (2018), 475-479.
  19. Ujjin, S. and Bentley, P.J., "Particle swarm optimization recommender system", in Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No. 03EX706), IEEE. (2003), 124-131.
  20. Kim, K.-j. and Ahn, H., "A recommender system using ga k-means clustering in an online shopping market", Expert Systems with Applications, Vol. 34, No. 2, (2008), 1200-1209.
  21. Spoorthy, G., "Hybrid cluster based collaborative filtering using firefly and agglomerative hierarchical clustering", International Journal of Computer and Information Technology (2279-0764), Vol. 10, No. 6, (2021).
  22. Kahrizi, M. and Kabudian, S., "Projectiles optimization: A novel metaheuristic algorithm for global optimization", International Journal of Engineering, Transactions A: Basics, Vol. 33, No. 10, (2020), 1924-1938.
  23. Zhang, J.-R., Zhang, J., Lok, T.-M. and Lyu, M.R., "A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training", Applied Mathematics and Computation, Vol. 185, No. 2, (2007), 1026-1037.
  24. Juang, C.-F., "A hybrid of genetic algorithm and particle swarm optimization for recurrent network design", IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 34, No. 2, (2004), 997-1006.
  25. Hassan, M. and Hamada, M., "A neural networks approach for improving the accuracy of multi-criteria recommender systems", Applied Sciences, Vol. 7, No. 9, (2017), 868.
  26. Kiran, R., Kumar, P. and Bhasker, B., "Dnnrec: A novel deep learning based hybrid recommender system", Expert Systems with Applications, Vol. 144, (2020), 113054.
  27. Gudise, V.G. and Venayagamoorthy, G.K., "Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks", in Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No. 03EX706), IEEE., (2003), 110-117.
  28. Yang, X.-S., "Firefly algorithms for multimodal optimization", in International symposium on stochastic algorithms, Springer., (2009), 169-178.
  29. Shambour, Q., "A deep learning based algorithm for multi-criteria recommender systems", Knowledge-Based Systems, Vol. 211, (2021), 106545.
  30. Gupta, S. and Kant, V., "An aggregation approach to multi-criteria recommender system using genetic programming", Evolving Systems, Vol. 11, No. 1, (2020), 29-44.
  31. Choudhary, P., Kant, V. and Dwivedi, P., "A particle swarm optimization approach to multi criteria recommender system utilizing effective similarity measures", in Proceedings of the 9th International Conference on Machine Learning and Computing. (2017), 81-85.
  32. Yang, X.-S. and He, X., "Firefly algorithm: Recent advances and applications", arXiv preprint arXiv:1308.3898, (2013).
  33. He, X., Liao, L., Zhang, H., Nie, L., Hu, X. and Chua, T.-S., "Neural collaborative filtering", in Proceedings of the 26th international conference on world wide web. (2017), 173-182.