Abstract
With the increase in amount of information, it becomes important to build recommendation systems which can map and provide the relevant information based on the preferences, tastes and trust of users. The data clustering is applied in recommendation system to reduce the computational overhead. It has been shown empirically that with the increase in number of clusters, the rating coverage decreases monotonically. To reduce the impact of clustering, the rating prediction is computed in terms of the user similarity, trust and Jaccard similarity with each term having some coefficient to give them weights. The optimal weights are decided for each clusters which are finally used to make the recommendation. Calculation of optimal parameters is one of the expensive steps and they are fixed for each users of the clusters. In this paper, we dynamically compute the optimal parameters for each pair of users instead of using static optimal parameters for each clusters. The optimal parameters in the proposed approach are individually calculated for two users according to the ratio of Pearson, trust and Jaccard similarity between them. It helps us to reduce the complexity of the system as well as it results into increasing the accuracy of overall recommendations. Experiment results on real datasets illustrate that the our improved 2D-Graph method defeats the competing approaches based on accuracy and rating coverage.
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References
Bellogin, A., Parapar, J.: Using graph partitioning techniques for neighbour selection in user-based collaborative filtering. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 213–216. ACM (2012)
Dakhel, G.M., Mahdavi, M.: A new collaborative filtering algorithm using K-means clustering and neighbors’ voting. In: 2011 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 179–184. IEEE (2011)
Di Noia, T., Rosati, J., Tomeo, P., Di Sciascio, E.: Adaptive multi-attribute diversity for recommender systems. Inf. Sci. 382, 234–253 (2017)
DuBois, T., Golbeck, J., Kleint, J., Srinivasan, A.: Improving recommendation accuracy by clustering social networks with trust. Recomm. Syst. Soc. Web 532, 1–8 (2009)
George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: Fifth IEEE International Conference on Data Mining (ICDM 2005), pp. 4–pp. IEEE (2005)
Guo, G., Zhang, J., Thalmann, D.: Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowl.-Based Syst. 57, 57–68 (2014)
Guo, G., Zhang, J., Yorke-Smith, N.: Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowl.-Based Syst. 74, 14–27 (2015)
Gupta, U., Patil, N.: Recommender system based on hierarchical clustering algorithm chameleon. In: 2015 IEEE International Advance Computing Conference (IACC), pp. 1006–1010. IEEE (2015)
Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397–406. ACM (2009)
Jiang, M., Cui, P., Wang, F., Zhu, W., Yang, S.: Scalable recommendation with social contextual information. IEEE Trans. Knowl. Data Eng. 26(11), 2789–2802 (2014)
Jothi, R., Mohanty, S.K., Ojha, A.: DK-means: a deterministic K-means clustering algorithm for gene expression analysis. Pattern Anal. Appl. 22(2), 649–667 (2019). https://doi.org/10.1007/s10044-017-0673-0
Liao, Q., Yang, F., Zhao, J.: An improved parallel K-means clustering algorithm with MapReduce. In: 2013 15th IEEE International Conference on Communication Technology, pp. 764–768. IEEE (2013)
Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 17–24. ACM (2007)
Moradi, P., Ahmadian, S., Akhlaghian, F.: An effective trust-based recommendation method using a novel graph clustering algorithm. Phys. A 436, 462–481 (2015)
Nilashi, M., Jannach, D., bin Ibrahim, O., Ithnin, N.: Clustering-and regression-based multi-criteria collaborative filtering with incremental updates. Inf. Sci. 293, 235–250 (2015)
Sheugh, L., Alizadeh, S.H.: A novel 2D-Graph clustering method based on trust and similarity measures to enhance accuracy and coverage in recommender systems. Inf. Sci. 432, 210–230 (2018)
Xu, B., Bu, J., Chen, C., Cai, D.: An exploration of improving collaborative recommender systems via user-item subgroups. In: Proceedings of the 21st International Conference on World Wide Web, pp. 21–30. ACM (2012)
Zhang, W., Wu, B., Liu, Y.: Cluster-level trust prediction based on multi-modal social networks. Neurocomputing 210, 206–216 (2016)
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Mishra, G., Kumar, S., Gupta, R., Mohanty, S.K. (2020). An Efficient Graph Based Trust-Aware Recommendation System. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1240. Springer, Singapore. https://doi.org/10.1007/978-981-15-6315-7_3
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DOI: https://doi.org/10.1007/978-981-15-6315-7_3
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