[1] Anandhan, A., Shuib, L., Ismail, M. A., & Mujtaba, G. (2018). Social media recommender systems: Review and open research issues. IEEE Access, 6, 15608–15628. https://doi.org/10.1109/ACCESS.2018.2810062
[2] Khusro, S., Ali, Z., & Ullah, I. (2016). Recommender systems: Issues, challenges, and research opportunities. In K. J. Kim & N. Joukov (Eds.), Information Science and Applications (ICISA) 2016 (Lecture Notes in Electrical Engineering, Vol. 376, pp. 1179–1189). Springer. https://doi.org/10.1007/978-981-10-0557-2_112
[3] Tang, J., Hu, X., & Liu, H. (2013). Social recommendation: A review. Social Network Analysis and Mining, 3(4), 1113–1133. https://doi.org/10.1007/s13278-013-0141-9
[4] Batmaz, Z., Yurekli, A., Bilge, A., & Kaleli, C. (2018). A review on deep learning for recommender systems: Challenges and remedies. Artificial Intelligence Review, 52(1), 1–37. https://doi.org/10.1007/s10462-018-9654-y
[5] Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., ... & Shah, H. (2016, September). Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems (pp. 7-10).
[6] Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132. https://doi.org/10.1016/j.knosys.2013.03.012
[7] Tang, J., Hu, X., & Liu, H. (2013). Social recommendation: A review. Social Network Analysis and Mining, 3(4), 1113–1133. https://doi.org/10.1007/s13278-013-0141-9 (Duplicate of [3]; recommend removing one)
[8] Ma, H., Yang, H., Lyu, M. R., & King, I. (2008, October). SoRec: Social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM ’08) (pp. 931–940). ACM. https://doi.org/10.1145/1458082.1458205
[9] Cai, C., He, R., & McAuley, J. (2017). SPMC: Socially-aware personalized Markov chains for sparse sequential recommendation. arXiv preprint arXiv:1708.04497. https://arxiv.org/abs/1708.04497
[10] Ma, H., Zhou, D., Liu, C., Lyu, M. R., & King, I. (2011). Recommender systems with social regularization. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (WSDM ’11) (pp. 287–296). ACM. https://doi.org/10.1145/1935826.1935877
[11] Guo, G., Zhang, J., & Yorke-Smith, N. (2015). TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15) (pp. 123–129). AAAI Press. https://doi.org/10.1609/aaai.v29i1.9153
[12] Zhao, T., McAuley, J., & King, I. (2014). Leveraging social connections to improve personalized ranking for collaborative filtering (SBPR). In Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM ’14) (pp. 261–270). ACM. https://doi.org/10.1145/2661829.2661998
[13] van den Berg, R., Kipf, T. N., & Welling, M. (2017). Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263. https://arxiv.org/abs/1706.02263
[14] Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W., & Leskovec, J. (2018). PinSage: Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18) (pp. 974–983). ACM. https://doi.org/10.1145/3219819.3219890
[15] Wang, M., Zheng, X., Yang, Y., & Zhang, K. (2018, April). Collaborative filtering with social exposure: A modular approach to social recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). AAAI Press. https://doi.org/10.1609/aaai.v32i1.11835
[16] Luo, C., Pang, W., & Wang, Z. (2014). Hete-CF: Social-based collaborative filtering using heterogeneous relations. arXiv preprint arXiv:1412.7610. https://arxiv.org/abs/1412.7610
[17] Jeckmans, A. J., Beye, M., Erkin, Z., Hartel, P. H., Lagendijk, R. L., & Tang, Q. (2013). Privacy in recommender systems. In Social Media Retrieval (pp. 263–281). Springer. https://doi.org/10.1007/978-1-4471-4555-4_12
[18] Aghasian, E., Garg, S., & Montgomery, J. (2018). Users’ privacy in recommendation systems applying online social network data: A survey and taxonomy. arXiv preprint arXiv:1806.07629. https://arxiv.org/abs/1806.07629
[19] Güneş, İ., Kaleli, C., Bilge, A., & Polat, H. (2014). Shilling attacks against recommender systems: A comprehensive survey. Artificial Intelligence Review, 42(4), 767–799. https://doi.org/10.1007/s10462-012-9364-9
[20] Zhou, W., Wen, J., Qu, Q., Zeng, J., & Cheng, T. (2018). Shilling attack detection for recommender systems based on credibility of group users and rating time series. PLOS ONE, 13(5), e0196533. https://doi.org/10.1371/journal.pone.0196533
[21] Zhou, W., Zhang, S., Zeng, J., Zeng, X., & Wang, Y. (2015). Shilling attacks detection in recommender systems based on target item analysis and SVM. Neurocomputing, 172, 387–396. https://doi.org/10.1016/j.neucom.2015.01.104
[22] Lee, S. K., Cho, Y. H., & Kim, S. H. (2010). Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Information Sciences, 180(11), 2142–2155. https://doi.org/10.1016/j.ins.2010.02.004
[23] Choi, K., Yoo, D., Kim, G., & Suh, Y. (2012). A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis. Electronic Commerce Research and Applications, 11(4), 309–317. https://doi.org/10.1016/j.elerap.2012.02.004
[24] Núñez-Valdéz, E. R., Cueva Lovelle, J. M., Sanjuán Martínez, O., García-Díaz, V., Ordoñez de Pablos, P., & Montenegro Marín, C. E. (2012). Implicit feedback techniques on recommender systems applied to electronic books. Computers in Human Behavior, 28(4), 1186–1193. https://doi.org/10.1016/j.chb.2012.02.001
[25] Maitham, J., & Qolizadeh, H. (2014). Improvement of recommender systems based on group refinement using social networks. In Proceedings of the 7th International Conference on Information Technology and Knowledge (Vol. 23). Urmia University.