Novel Approach of Neural Collaborative Filter by Pairwise Objective Function with Matrix Factorization

  • Authors

    • Ram Sethuraman
    • Akshay havalgi
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.17840
  • NCF, Gaussian, movielens

  • The concept of deep learning is used in the various fields like text, speech and vision. The proposed work deep neural network is used for recommender system. In this work pair wise objective function is used for emphasis of non-linearity and latent features. The GMF (Gaussian matrix factorization) and MLP techniques are used in this work. The proposed framework is named as NCF which is basically neural network based collaborative filtering. The NCF gives the latent features by reducing the non-linearity and generalizing the matrix. In the proposed work combination of pair-wise and point wise objective function is used and tune by using the concept of cross entropy with Adam optimization. This optimization approach optimizes the gradient descent function. The work is done on 1K and 1M movies lens dataset and it is compared with deep matrix factorization (DMF).

     

     

  • References

    1. [1] Shah, Mit, Dhruvesh Parikh, and Bharat Deshpande. "Movie Recommendation System Employing Latent Graph Features in Extremely Randomized Trees." Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies.ACM, 2016.

      [2] Schnabel, Tobias, et al. "Using shortlists to support decision making and improve recommender system performance." Proceedings of the 25th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee, 2016.

      [3] Singh, Gurpreet, and Rajdavinder Singh Boparai. "A Novel Hybrid Music Recommendation System using K-Means Clustering and PLSA." Indian Journal of Science and Technology 9.28 (2016).

      [4] Nilashi, Mehrbakhsh, Othman Ibrahim, and KaramollahBagherifard. "A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques." Expert Systems with Applications 92 (2018): 507-520.

      [5] Sulthana, A. Razia, and SubburajRamasamy. "Ontology and context based recommendation system using Neuro-Fuzzy Classification." Computers & Electrical Engineering (2018).

      [6] Wei, Jian, et al. "Collaborative filtering and deep learning based recommendation system for cold start items." Expert Systems with Applications 69 (2017): 29-39.

      [7] Katarya, Rahul, and Om PrakashVerma. "An effective collaborative movie recommender system with cuckoo search." Egyptian Informatics Journal 18.2 (2017): 105-112.

      [8] He, Xiangnan, et al. "Neural collaborative filtering." Proceedings of the 26th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee, 2017.

      [9] Kermany, NaimeRanjbar, and Sasan H. Alizadeh. "A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques." Electronic Commerce Research and Applications 21 (2017): 50-64.

      [10] Najafabadi, Maryam Khanian, et al. "Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data." Computers in Human Behavior 67 (2017): 113-128.

      [11] Wang, Zhibo, et al. "Friendbook: a semantic-based friend recommendation system for social networks." IEEE Transactions on Mobile Computing 14.3 (2015): 538-551.

      [12] Sohail, ShahabSaquib, JamshedSiddiqui, and Rashid Ali. "User feedback based evaluation of a product recommendation system using rank aggregation method." Advances in Intelligent Informatics.Springer, Cham, 2015.349-358.

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  • How to Cite

    Sethuraman, R., & havalgi, A. (2018). Novel Approach of Neural Collaborative Filter by Pairwise Objective Function with Matrix Factorization. International Journal of Engineering & Technology, 7(3.12), 1213-1217. https://doi.org/10.14419/ijet.v7i3.12.17840