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World Conference on Information Systems and Technologies

WorldCIST'18 2018: Trends and Advances in Information Systems and Technologies pp 122–132Cite as

Sentiment Analysis of Social Network Data for Cold-Start Relief in Recommender Systems

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 746))

Abstract

Recommender systems have been used in e-commerce to increase conversion due to matching product offer and consumer preferences. Cold-start is the situation of a new user about whom there is no information to make suitable recommendations. Texts published by the user in social networks are a good source of information to reduce the cold-start issue. However, the valence of the emotion in a text must be considered in the recommendation so that no product is recommended based on a negative opinion. This paper proposes a recommendation process that includes sentiment analysis to textual data extracted from Facebook and Twitter and present results of an experiment in which this algorithm is used to reduce the cold-start issue.

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References

  1. Jain, S., Grover, A., Thakur, P.S., Choudhary, S.K.: Trends, problems and solutions of recommender system. In: International Conference on Computing, Communication & Automation. pp. 955–958 (2015)

    Google Scholar 

  2. Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)

    Article  Google Scholar 

  3. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7, 76–80 (2003)

    Article  Google Scholar 

  4. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR 2002, vol. 46, pp. 253–260 (2002)

    Google Scholar 

  5. Zhang, Y., Pennacchiotti, M.: Predicting purchase behaviors from social media. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1521–1532 (2013)

    Google Scholar 

  6. Dang, T.T., Duong, T.H., Nguyen, H.S.: A hybrid framework for enhancing correlation to solve cold-start problem in recommender systems. In: Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), pp. 1–5 (2014)

    Google Scholar 

  7. Fernández-Tobías, I., Braunhofer, M., Elahi, M., et al.: Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model User-Adapt Interact. 26, 1–35 (2016)

    Article  Google Scholar 

  8. Prando, A.V., Contratres, F., Souza, S., De Souza, L.: Content-based recommender system using social networks for cold-start users. In: Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, pp. 181–189 (2017)

    Google Scholar 

  9. Zhang, Y., Pennacchiotti, M.: Recommending branded products from social media. In: Proceedings of 7th ACM Conference on Recommender Systems - RecSys 2013, pp. 77–84 (2013)

    Google Scholar 

  10. Jamiy, F., Daif, A., Azouazi, M., Marzak, A.: The potential and challenges of big data - Recommendation systems next level application. IJCSI Int. J. Comput. Sci. 11 (2015)

    Google Scholar 

  11. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)

    Article  Google Scholar 

  12. Konstan, A.J.: Introduction to recommender systems. ACM Trans. Inf. Syst. 22, 1–4 (2004)

    Article  Google Scholar 

  13. Colace, F., De Santo, M., Greco, L., et al.: A collaborative user-centered framework for recommending items in online social networks. Comput. Hum. Behav. 51, 694–704 (2015)

    Article  Google Scholar 

  14. Lalwani, D., Somayajulu, D.V.L.N., Krishna, P.R.: A community driven social recommendation system. In: 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, pp. 821–826 (2015)

    Google Scholar 

  15. Maniktala, M., Sachdev, S., Bansal, N., Susan, S.: Finding the most informational friends in a social network based recommender system. In: Annual IEEE India Conference (INDICON), New Delhi, pp. 1–6 (2015)

    Google Scholar 

  16. Ma, H., Zhou, D., Liu, C., et al.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining - WSDM 2011, p. 287. ACM Press, New York (2011)

    Google Scholar 

  17. Oliveira, A.D.R., Bessa, L.N., Andrade, T.R., et al.: Trust-based recommendation for the social web. IEEE Lat. Am. Trans. 10, 1661–1666 (2012)

    Article  Google Scholar 

  18. Felicio, C.Z., Paixao, K.V.R., Barcelos, C.A.Z., Preux, P.: Preference-like score to cope with cold-start user in recommender systems. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 62–69. IEEE (2016)

    Google Scholar 

  19. Quirino, G.Z., Mals, N.P., Groterhorst, V.M., et al.: Meneduca — social school network to support the educational environment. In: 2015 Latin American Computing Conference (CLEI), pp. 1–6. IEEE (2015)

    Google Scholar 

  20. Yadollahi, A., Shahraki, A.G., Zaiane, O.R.: Current state of text sentiment analysis from opinion to emotion mining. ACM Comput. Surv. 50, 1–33 (2017)

    Article  Google Scholar 

  21. Alahmadi, D.H., Zeng, X.J.: Twitter-based recommender system to address cold-start: a genetic algorithm based trust modelling and probabilistic sentiment analysis. In: Proceedings of the International Conference on Tools with Artificial Intelligence ICTAI 2016, pp. 1045–1052, January 2016

    Google Scholar 

  22. Krauss, C., Braun, S., Arbanowski, S.: Preference ontologies based on social media for compensating the cold start problem. In: Proceedings of the 8th Workshop on Social Network Mining Analysis – SNAKDD 2014, pp. 1–4 (2014)

    Google Scholar 

  23. Ibrahim, S.Z., Blandford, A., Bianchi-Berthouze, N.: Privacy settings on Facebook: their roles and importance. In: Proceedings - 2012 IEEE International Conference on Green Computing and Communications, GreenCom 2012, Conference on Internet of Things, iThings 2012 and Conference on Cyber, Physical and Social Computing, CPSCom 2012 (2012)

    Google Scholar 

  24. Derczynski, L., Maynard, D., Rizzo, G., et al.: Analysis of named entity recognition and linking for tweets. Inf. Process Manag. 51, 32–49 (2015)

    Article  Google Scholar 

  25. Gupellil, I., Boukhalfa, K.: Social big data mining: a survey focused on opinion mining and sentiments analysis. In: 12th International Symposium on Programming and Systems, ISPS 2015, pp. 132–141 (2015)

    Google Scholar 

  26. Colace, F., De Santo, M., Greco, L.: A probabilistic approach to tweets’ sentiment classification. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 37–42. IEEE (2013)

    Google Scholar 

  27. Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)

    Google Scholar 

  28. Hotho, A., Nürnberger, A., Paaß, G.: A brief survey of text mining. LDV Forum - Gld J. Comput. Linguist. Lang. Technol. 20, 19–62 (2005)

    Google Scholar 

  29. Hu, X., Liu, H.: Text analytics in social media. In: Mining Text Data, pp. 385–414. Springer, Boston (2012)

    Chapter  Google Scholar 

  30. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)

    MATH  Google Scholar 

  31. Singh, V., Dubey, S.K.: Opinion mining and analysis: a literature review. In: 2014 5th International Conference - Confluence the Next Generation Information Technology Summit (Confluence), pp. 232–239 (2014)

    Google Scholar 

  32. Li, W., Sun, B.: An improved collaborative filtering recommendation algorithm incorporating opinions analysis. In: Proceedings - 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2015, pp. 171–173 (2015)

    Google Scholar 

  33. Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011)

    MATH  Google Scholar 

  34. Krejcie, R.V., Morgan, D.: Determining sample size for research activities. Educ. Psychol. Meas. 30, 607–610 (1970). https://doi.org/10.1177/001316447003000308

    Article  Google Scholar 

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Acknowledgments

Authors are grateful for the support given by São Paulo Research Foundation (FAPESP). Grant #2014/04851-8, São Paulo Research Foundation (FAPESP).

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Authors and Affiliations

  1. Universidade de São Paulo - Escola Politécnica, São Paulo, Brazil

    Felipe G. Contratres, Solange N. Alves-Souza & Lucia Vilela Leite Filgueiras

  2. Faculdade de Tecnologia, São Paulo, Brazil

    Luiz S. DeSouza

Authors
  1. Felipe G. Contratres
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  2. Solange N. Alves-Souza
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  3. Lucia Vilela Leite Filgueiras
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  4. Luiz S. DeSouza
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Corresponding author

Correspondence to Felipe G. Contratres .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

  3. DSI/EEUM, Universidade do Minho, Guimarães, Portugal

    Luís Paulo Reis

  4. DIMES, Universita della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

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Contratres, F.G., Alves-Souza, S.N., Filgueiras, L.V.L., DeSouza, L.S. (2018). Sentiment Analysis of Social Network Data for Cold-Start Relief in Recommender Systems. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-319-77712-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-77712-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77711-5

  • Online ISBN: 978-3-319-77712-2

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