A Social Recommender System Based on Exponential Random Graph Model and Sentiment Similarity

Abstract:

With the rapid growth of social media, recommendation for social activities is urgently needed to overcome information overload. Micro-blog, as one of most popular social media platform, has not a good enough recommender approach to satisfy users expectation. In this paper, we proposed a social recommender system using both exponential random graph model and sentiment similarity. Firstly, we built a good fitted graph model that was used to predict the probabilities of non-linked nodes. Moreover, we collected contents of each user for mining their emotions and select 106 features. Karhunen-Loéve transform (KLT) was chose to analyze the features of those texts. Based on KLT, average distances of text features were used to calculate the sentiment similarity. Therefore, according to the resort of similarities, we gave top-N recommendation for user. Finally, we studied this social recommender system on diabetes micro-blog. The metrics showed that our proposed social recommender system outperform other methods.

• Periodical: Applied Mechanics and Materials (Volumes 488-489)
• Pages: 1326-1330
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.488-489.1326
• Online since: January 2014
• Authors: Dong Hui Yang*, Yi Su
• Keywords: Micro-Blog, Sentiment Similarity, Social Network Analysis (SNA), Social Recommender System

References

[1] Guy, I., D. Carmel. Social recommender systems. In: Proceedings of the 20th international conference companion on World wide web. ACM. (2011).

DOI: 10.1145/1963192.1963312

[2] He, J., W.W. Chu: A social network-based recommender system (SNRS). Springer. (2010).

[3] Feldman R, Sanger J: The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press(2007).

DOI: 10.1017/cbo9780511546914

[4] P Bonhard, M A Sasse. Knowing me, knowing you, — using profiles and social networking to improve recommender systems. BT Technology Journal, 24(3): 84-98. (2006).

DOI: 10.1007/s10550-006-0080-3

[5] PS Chang, I-H Ting, SL Wang: Towards Social Recommendation System Based on The Data From Microblogs. International Conference on Advances in Social Networks Analysis and Mining. pp.672-677. (2011).

DOI: 10.1109/asonam.2011.101

[6] J. O'Donovan,B. Smyth: Trust in Recommender Systems. Proc. 10th Int'l Conf. Intelligent User Interfaces, pp.167-174, ACM Press. (2005).

[7] Walter F, Battiston S, Schweitzer F: A model of a trust-based recommendation system on a social network. Journal of Autonomous Agents and Multi-Agent Systems, 16(1): 57-74. (2008).

DOI: 10.1007/s10458-007-9021-x

[8] R Zhang, B Sun, W Kang. GLOSS: A Social Networks-Based Recommender System. 5th International Conference on Pervasive Computing and Applications. Slovenia, pp.122-127. (2010).

DOI: 10.1109/icpca.2010.5704086

[9] Armentano M G, Godoy D, Amandi A A. Recommending information sources to information seekers in Twitter, International Workshop on Social Web Mining. (2011).