Social recommender systems: techniques, domains, metrics, datasets and future scope
- 44 Downloads
With the evolution of social media, an enormous amount of information is shared every day. Recommender systems contribute significantly in handling big data and presenting relevant information, services and items to people. A substantial number of recommender system algorithms based on social media data have been proposed and applied to numerous domains in the literature. This paper presents a state-of-the-art survey of existing techniques of social recommender systems. We present different domains where the existing systems have been experimented. We also present a tabular representation of different metrics used by these papers. We discuss some frequently used datasets of these systems. Lastly, we discuss some of the future works in this area. The main aim of this paper is to provide a concise review of published papers to assist potential researchers in this field to devise new techniques.
KeywordsRecommender system Social media Collaborative filtering Deep learning Social networks Social recommender system
- Ahmadian, S., Joorabloo, N., Jalili, M., Meghdadi, M., Afsharchi, M., Ren, Y. (2018a). A temporal clustering approach for social recommender systems. In 2018 IEEE/ACM International conference on advances in social networks analysis and mining (ASONAM) (pp. 1139–1144): IEEE.Google Scholar
- Atanassov, K.T. (1999). Intuitionistic fuzzy sets. In Intuitionistic fuzzy sets (pp. 1–137): Springer.Google Scholar
- Dang, Q.V., & Ignat, C.L. (2017). dTrust: a simple deep learning approach for social recommendation. In The 3Rd IEEE International Conference on Collaboration and Internet Computing (CIC-17). United States: San Jose.Google Scholar
- Fan, W., Li, Q., Cheng, M. (2018). Deep modeling of social relations for recommendation. In Thirty-Second AAAI Conference on Artificial Intelligence.Google Scholar
- Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D. (2019). Graph neural networks for social recommendation. arXiv:190207243.
- Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S. (2013). Toward the next generation of recommender systems: applications and research challenges. In Multimedia services in intelligent environments (pp. 81–98): Springer.Google Scholar
- Frikha, M., Mhiri, M., Gargouri, F. (2015). Designing A user interest ontology-driven social recommender system: Application for tunisian tourism. In Trends in practical applications of agents, Multi-Agent Systems and Sustainability (pp. 159–166): Springer.Google Scholar
- Frikha, M., Mhiri, M.B.A., Gargouri, F., et al. (2017). Social trust based semantic tourism recommender system: a case of medical tourism in tunisia. European Journal of Tourism Research, 17, 59–82.Google Scholar
- García-Sánchez, F., García-Díaz, J.A., Gómez-Berbís, J.M., Valencia-García, R. (2018). Ontology-based advertisement recommendation in social networks. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 36–44): Springer.Google Scholar
- Geng, X., Zhang, H., Bian, J., Chua, T.S. (2015). Learning image and user features for recommendation in social networks. In 2015 IEEE International Conference on Computer Vision (ICCV) (pp. pp 4274–4282). https://doi.org/10.1109/ICCV.2015.486.
- Ghasemi, T. (2012). Fuzzy thesauri recommendation system for web 2.0 social networks.Google Scholar
- Golbeck, J. (2006a). Combining provenance with trust in social networks for semantic web content filtering. In International Provenance and Annotation Workshop (pp. 101–108): Springer.Google Scholar
- Golbeck, J., Hendler, J., et al. (2006b). Filmtrust: Movie recommendations using trust in web-based social networks. In Proceedings of the IEEE Consumer communications and networking conference (pp. 282–286): Citeseer.Google Scholar
- Gottapu, R.D., & Monangi, L.V.S. (2017). Point-of-interest recommender system for social groups. Procedia Computer Science, 114, 159–164. https://doi.org/10.1016/j.procs.2017.09.020, complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS October 30 November 1, 2017, Chicago, Illinois, USA.CrossRefGoogle Scholar
- Guan, C., Fung, Y.K.K., Yue, Y. (2018). Towards a Personalized Item Recommendation Approach in Social Tagging Systems Using Intuitionistic Fuzzy DBSCAN. In 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (pp. vol. 1, pp 361–364): IEEE.Google Scholar
- Guy I. (2015). Social recommender systems. In Recommender systems handbook (pp. 511–543): Springer.Google Scholar
- Jamali, M., & Ester, M. (2010). A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys ’10 (pp. 135–142). New York: ACM. https://doi.org/10.1145/1864708.1864736.
- Knijnenburg, B.P., Bostandjiev, S., O’Donovan, J., Kobsa, A. (2012). Inspectability and control in social recommenders. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys ’12 (pp. 43–50). New York: ACM.. https://doi.org/10.1145/2365952.2365966
- Konstas, I., Stathopoulos, V., Jose, J.M. (2009). On social networks and collaborative recommendation. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 195–202): ACM.Google Scholar
- Li, H., Wu, D., Tang, W., Mamoulis, N. (2015). Overlapping community regularization for rating prediction in social recommender systems. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 27–34): ACM.Google Scholar
- Liu, N.N., He, L., Zhao, M. (2013). Social temporal collaborative ranking for context aware movie recommendation. ACM Transactions on Intelligent Systems and Technology (TIST), 4(1), 15.Google Scholar
- Ma, H., Yang, H., Lyu, M.R., King, I. (2008). SoRec: Social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM ’08 (pp. 931–940). New York: ACM. https://doi.org/10.1145/1458082.1458205.
- Ma, H., King, I., Lyu, M.R. (2009a). Learning to recommend with social trust ensemble. In Proceedings of the 32Nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’09 (pp. 203–210). New York: ACM. https://doi.org/10.1145/1571941.1571978.
- Ma, H., Lyu, M.R., King, I. (2009b). Learning to recommend with trust and distrust relationships. In Proceedings of the Third ACM Conference on Recommender Systems, RecSys ’09 (pp. 189–196). New York: ACM. https://doi.org/10.1145/1639714.1639746.
- 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). New York: ACM. https://doi.org/10.1145/1935826.1935877.
- Pan, R., Dolog, P., Xu, G. (2012). KNN-based clustering for improving social recommender systems. In International Workshop on Agents and Data Mining Interaction (pp. 115–125): Springer.Google Scholar
- Pham, M.C., Cao, Y., Klamma, R., Jarke, M. (2011). A clustering approach for collaborative filtering recommendation using social network analysis. Journal of Universal Computer Science, 17(4), 583–604.Google Scholar
- Qin, D., Zhou, X., Chen, L., Huang, G., Zhang, Y. (2018). Dynamic connection-based social group recommendation. IEEE Transactions on Knowledge and Data Engineering, 1–14. https://doi.org/10.1109/TKDE.2018.2879658.
- Quijano-Sanchez, L., Recio-Garcia, J.A., Diaz-Agudo, B., Jimenez-Diaz, G. (2013). Social factors in group recommender systems. ACM Transactions on Intelligent Systems and Technology (TIST), 4(1), 8.Google Scholar
- Sedhain, S., Menon, A.K., Sanner, S., Xie, L. (2015). Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web (pp. 111–112): ACM.Google Scholar
- Sellami, K., Ahmed-Nacer, M., Tiako, P. (2014). From social network to semantic social network in recommender system. arXiv:14073392.
- Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. In Recommender systems handbook (pp. 257–297): Springer.Google Scholar
- Shen, Y., Lv, T., Chen, X., Wang, Y. (2016). A collaborative filtering based social recommender system for e-commerce. International Journal of Simulation: Systems, Science and Technology, 17(22), 91–96.Google Scholar
- Sheugh, L., & Alizadeh, H.S. (2015). Merging similarity and trust based social networks to enhance the accuracy of trust-aware recommender systems. Journal of Computer & Robotics, 8(2), 43–51.Google Scholar
- Shokeen, J., & Rana, C. (2017). Fuzzy sets, advanced fuzzy sets and hybrids. In 2017 International conference on energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 2538–2542). https://doi.org/10.1109/ICECDS.2017.8389911.
- Shokeen, J., & Rana, C. (2018b). A study on trust-aware social recommender systems. In A study on Trust-aware Social Recommender Systems (pp. 4268–4272); IEEE.Google Scholar
- Shokeen, J., & Rana, C. (2019b). A study on features of social recommender systems. Artificial Intelligence Review. https://doi.org/10.1007/s10462-019-09684-w.
- Shokeen, J., Rana, C., Sehrawat, H. (2019c). A novel approach for community detection using the label propagation technique. In Integrated intelligent computing, Communication and Security (pp. 127–132): Springer.Google Scholar
- Tang, L., Cai, D., Duan, Z., Ma, J., Han, M., Wang, H. (2019). Discovering travel community for POI recommendation on location-based social networks. Complexity 2019.Google Scholar
- Wang, C., & Blei, D.M. (2011). Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’11 (pp. 448–456). New York: ACM. https://doi.org/10.1145/2020408.2020480.
- Wang, H., Wang, N., Yeung, D.Y. (2015a). Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1235–1244): ACM.Google Scholar
- Wang, X., He, X., Nie, L., Chua, T.S. (2017). Item silk road: Recommending items from information domains to social users. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’17 (pp. pp 185–194). New York: CM. https://doi.org/10.1145/3077136.3080771.
- Xu, Z., Lukasiewicz, T., Chen, C., Miao, Y., Meng, X. (2017). Tag-aware personalized recommendation using a hybrid deep model. In AAAI Press/International Joint Conferences on Artificial Intelligence.Google Scholar
- Yang, X., Steck, H., Guo, Y., Liu, Y. (2012a). On top-k recommendation using social networks. In Proceedings of the sixth ACM conference on Recommender systems (pp. 67–74): ACM.Google Scholar
- Yang, X., Steck, H., Liu, Y. (2012b). Circle-based recommendation in online social networks. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12 (pp. 1267–1275). New York: ACM. https://doi.org/10.1145/2339530.2339728.
- Ying, H., Chen, L., Xiong, Y., Wu, J. (2016). Collaborative deep ranking: a hybrid pair-wise recommendation algorithm with implicit feedback. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 555–567): Springer.Google Scholar
- Zhang, S., Yao, L., Sun, A. (2017). Deep learning based recommender system: a survey and new perspectives. CoRR arXive:1707.07435.
- Zheng, Y., Mobasher, B., Burke, R.D. (2013). The role of emotions in context-aware recommendation. In ACM Conference on Recommender Systems, RecSys ’13 (pp. 21–28). Hong Kong.Google Scholar