Abstract
With the rapid advancement of Internet, e-commerce websites and social networks, people prefer to receive recommendations from their social friends rather than strangers. Also, the exponential evolution and use of online social networks has resulted in generation of enormous amount of information over web. The relationships between users in social networks are complex, vague and uncertain for computation. Adhering to the intuition that a user’s social network plays a prominent role in influencing the personal behavior of users on web, this paper proposes a trust and semantic-based social recommendation approach to remove cold-start issues. Social relationships are used to compute trust between users in the social networks. Trusted relations are used in addition to rating matrix to extract the implicit data. For each user, we also attempt to discover the top-k semantic friends because a user is connected to multiple friends on social networks who have different tastes. This proposed approach is superior to those traditional approaches that give equal weights to all users in social networks. One important advantage of this approach is consideration of social friends at different levels. Experimental results on real-world dataset demonstrate that our proposed approach outperforms some of the state-of-the-art recommendation approaches.
Similar content being viewed by others
References
Bathla G, Aggarwal H, Rani R (2019) Using deep learning to improve recommendation with direct and indirect social trust. J Stat Manag Syst 22(4):665–677
Bauer DF (1972) Constructing confidence sets using rank statistics. J Am Stat Assoc 67(339):687–690
Bellogín A, Cantador I, Castells P, Díez F (2013) Exploiting social networks in recommendation: a multi-domain comparison. In: DIR, Citeseer, pp 40–41
Colombo-Mendoza LO, Valencia-García R, Rodríguez-González A, Colomo-Palacios R, Alor-Hernández G (2018) Towards a knowledge-based probabilistic and context-aware social recommender system. J Inf Sci 44(4):464–490
Cui L, Sun L, Fu X, Lu N, Zhang G (2017) Exploring a trust based recommendation approach for videos in online social network. J Signal Process Syst 86(2–3):207–219
Dakhel AM, Malazi HT, Mahdavi M (2018) A social recommender system using item asymmetric correlation. Appl Intell 48(3):527–540
Dang QV, Ignat CL (2017) dTrust: a simple deep learning approach for social recommendation. In: 3rd IEEE international conference on collaboration and internet computing, pp 209–218. https://doi.org/10.1109/CIC.2017.00036
Davoodi E, Kianmehr K, Afsharchi M (2013) A semantic social network-based expert recommender system. Appl Intell 39(1):1–13. https://doi.org/10.1007/s10489-012-0389-1
Dong Y, Chawla NV, Swami A (2017) metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 135–144
Gao P, Miao H, Baras JS, Golbeck J (2016) STAR: semiring trust inference for trust-aware social recommenders. In: Proceedings of the 10th ACM conference on recommender systems. ACM, New York, NY, USA, RecSys’16, pp 301–308. https://doi.org/10.1145/2959100.2959148
García-Sánchez F, Colomo-Palacios R, Valencia-García R (2020) A social-semantic recommender system for advertisements. Inf Process Manag 57(2):102–153
Golbeck J (2006) Generating predictive movie recommendations from trust in social networks. In: International conference on trust management. Springer, pp 93–104
Golbeck J, Hendler J (2006) Filmtrust: Movie recommendations using trust in web-based social networks. Proc IEEE Consum Commun Netw Conf 96:282–286
Gonzalez Camacho LA, Alves-Souza SN (2018) Social network data to alleviate cold-start in recommender system: a systematic review. Inf Process Manag 54(4):529–544. https://doi.org/10.1016/j.ipm.2018.03.004
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. AAAI 15:123–125
Guo L, Wen YF, Wang XH (2018) Exploiting pre-trained network embeddings for recommendations in social networks. J Comput Sci Technol 33:682–696. https://doi.org/10.1007/s11390-018-1849-9
Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM conference on recommender systems, pp 135–142. https://doi.org/10.1145/1864708.1864736
Konstas I, Stathopoulos V, Jose JM (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
Koren Y (2010) Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans Knowl Discov Data (TKDD) 4(1):1–24
Lee DH, Brusilovsky P (2009) Does trust influence information similarity. In: RecSys 2009
Lee K, Lee K (2015) Escaping your comfort zone: a graph-based recommender system for finding novel recommendations among relevant items. Expert Syst Appl 42(10):4851–4858
Li YM, Wu CT, Lai CY (2013) A social recommender mechanism for e-commerce: combining similarity, trust, and relationship. Decis Support Syst 55(3):740–752
Li Y, Liu J, Ren J (2019) Social recommendation model based on user interaction in complex social networks. PLoS ONE 14(7):e0218957. https://doi.org/10.1371/journal.pone.0218957
Liao Q, Wang B, Ling Y, Zhao J, Qiu X (2015) Improved recommendation system using friend relationship in SNS. In: Transactions on computational collective intelligence XIX, vol 9380. Springer, New York, pp 17–31
Ma H, Yang H, Lyu MR, King I (2008) SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM conference on information and knowledge management, Association for Computing Machinery, New York, NY, USA, CIKM ’08, pp 931–940. https://doi.org/10.1145/1458082.1458205
Ma H, King I, Lyu MR (2009) Learning to recommend with social trust ensemble. In: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pp 203–210
Ma H, King I, Lyu MR (2011a) Learning to recommend with explicit and implicit social relations. ACM Trans Intell Syst Technol (TIST) 2(3):1–19
Ma H, Zhou D, Liu C, Lyu MR, King I (2011b) Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on web search and data mining, Association for Computing Machinery, New York, NY, USA, pp 287–296. https://doi.org/10.1145/1935826.1935877
McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27(1):415–444
Mei JP, Yu H, Shen Z, Miao C (2017) A social influence based trust model for recommender systems. Intell Data Anal 21(2):263–277
Melville P, Sindhwani V (2011) Recommender systems. In: Encyclopedia of machine learning. Springer, pp 829–838
Pitsilis G, Knapskog SJ (2009) Social trust as a solution to address sparsity-inherent problems of recommender systems. In: RecSys 2009
Rana C, Jain SK (2014) An evolutionary clustering algorithm based on temporal features for dynamic recommender systems. Swarm Evol Comput 14:21–30
Rana C, Jain SK (2015) A study of the dynamic features of recommender systems. Artif Intell Rev 43(1):141–153
Rani P, Shokeen J (2017) Issues and challenges in link prediction for social networks. In: Proceedings of the 11th INDIACom and 4th international conference on computing for sustainable global development. IEEE, pp 6889–6895
Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40(3):56–58. https://doi.org/10.1145/245108.245121
Salakhutdinov R, Mnih A (2008) Probabilistic matrix factorization. In: Advances in neural information processing systems, pp 1257–1264
Shi C, Hu B, Zhao WX, Philip SY (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357–370
Shokeen J (2018) On measuring the role of social networks in project recommendation. Int J Comput Sci Eng 6(4):215–219
Shokeen J, Rana C (2018a) A review on the dynamics of social recommender systems. Int J Web Eng Technol 13(3):255–276. https://doi.org/10.1504/IJWET.2018.095184
Shokeen J, Rana C (2018b) A study on trust-aware social recommender systems. In: Proceedings of the 12th INDIACom and 5th international conference on computing for sustainable global development. IEEE, pp 4268–4272
Shokeen J, Rana C (2019) An application-oriented review of deep learning in recommender systems. Int J Intell Syst Appl 11(5):46. https://doi.org/10.5815/ijisa.2019.05.06
Shokeen J, Rana C (2020a) Social recommender systems: techniques, domains, metrics, datasets and future scope. J Intell Inf Syst 54(3):633–667. https://doi.org/10.1007/s10844-019-00578-5
Shokeen J, Rana C (2020b) A study on features of social recommender systems. Artif Intell Rev 53(2):965–988. https://doi.org/10.1007/s10462-019-09684-w
Shokeen J, Rana C, Rani P (2020) A trust-based approach to extract social relationships for recommendation. In: International conference on data analytics and management: an indo-european conference (ICDAM-2020). Springer, pp 1–8
Sulieman D, Malek M, Kadima H, Laurent D (2016) Toward social-semantic recommender systems. Int J Inf Syst Soc Change (IJISSC) 7(1):1–30
Taheri SM, Mahyar H, Firouzi M, Ghalebi K E, Grosu R, Movaghar A (2017) Extracting implicit social relation for social recommendation techniques in user rating prediction. In: Proceedings of the 26th international conference on world wide web companion, pp 1343–1351
Tang J, Hu X, Gao H, Liu H (2013a) Exploiting local and global social context for recommendation. In: Proceedings of the twenty-third international joint conference on artificial intelligence, Beijing, China, IJCAI ’13, pp 2712–2718
Tang J, Hu X, Liu H (2013b) Social recommendation: a review. Soc Netw Anal Min 3(4):1113–1133
Tarus JK, Niu Z, Yousif A (2017) A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Gener Comput Syst 72:37–48
Tian H, Liang P (2017) Improved recommendations based on trust relationships in social networks. Future Internet 9(1):9
Wang M, Ma J (2016) A novel recommendation approach based on users’ weighted trust relations and the rating similarities. Soft Comput 20(10):3981–3990. https://doi.org/10.1007/s00500-015-1734-1
Yuan W, Shu L, Chao HC, Guan D, Lee YK, Lee S (2010) ITARS: trust-aware recommender system using implicit trust networks. IET Commun 4(14):1709–1721
Zhang C, Yu L, Wang Y, Shah C, Zhang X (2017) Collaborative user network embedding for social recommender systems. In: Proceedings of the 2017 SIAM international conference on data mining, pp 381–389. https://doi.org/10.1137/1.9781611974973.43
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shokeen, J., Rana, C. A trust and semantic based approach for social recommendation. J Ambient Intell Human Comput 12, 10289–10303 (2021). https://doi.org/10.1007/s12652-020-02806-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02806-1