Advertisement

User Modeling and User-Adapted Interaction

, Volume 28, Issue 3, pp 277–329 | Cite as

Inferring user interests in microblogging social networks: a survey

  • Guangyuan Piao
  • John G. Breslin
Article

Abstract

With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain.

Keywords

User modeling User interests User profiles Social web Microblogging Twitter Social networks Information filtering Recommender systems Personalization Survey 

Notes

Acknowledgements

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight Centre for Data Analytics). Thanks for the anonymous reviewers and the editor for their constructive feedback to improve this work.

References

  1. Abdel-Hafez, A., Xu, Y.: A survey of user modelling in social media websites. Comput. Inf. Sci. 6(4), 59–71 (2013)Google Scholar
  2. Abel, F.: Contextualization, user modeling and personalization in the social web—from social tagging via context to cross-system user modeling and personalization. PhD thesis, Leibniz University of Hanover (2011)Google Scholar
  3. Abel, F., Gao, Q., Houben, G.J., Tao, K.: Analyzing temporal dynamics in Twitter profiles for personalized recommendations in the social web. In: Proceedings of the 3rd International Web Science Conference, Koblenz, Germany, pp. 1–8. ACM (2011a)Google Scholar
  4. Abel, F., Gao, Q., Houben, G.J., Tao, K.: Analyzing user modeling on Twitter for personalized news recommendations. In: User Modeling, Adaption and Personalization, Girona, Spain, pp. 1–12. Springer (2011b)Google Scholar
  5. Abel, F., Gao, Q., Houben, G.J., Tao, K.: Semantic enrichment of Twitter posts for user profile construction on the social web. In: The Semantic Web: Research and Applications: 8th Extended Semantic Web Conference, ESWC 2011, Heraklion, Crete, Greece, pp. 375–389. Springer (2011c)Google Scholar
  6. Abel, F., Hauff, C., Houben, G.J., Tao, K.: Leveraging user modeling on the social web with linked data. In: Web Engineering: 12th International Conference, ICWE 2012, Berlin, Germany, pp. 378–385. Springer (2012)Google Scholar
  7. Abel, F., Gao, Q., Houben, G.J., Tao, K.: Twitter-based user modeling for news recommendations. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI ’13, Beijing, China, pp. 2962–2966. AAAI Press (2013a)Google Scholar
  8. Abel, F., Herder, E., Houben, G.J., Henze, N., Krause, D.: Cross-system user modeling and personalization on the social web. User Model. User Adapt. Interact. 23(2–3), 169–209 (2013b)CrossRefGoogle Scholar
  9. Ahmed, A., Low, Y., Aly, M., Josifovski, V., Smola, A.J.: Scalable distributed inference of dynamic user interests for behavioral targeting. In: Proceedings of the 17th International Conference on Knowledge Discovery and Data Mining, , San Diego, CA, USA, pp. 114–122. ACM (2011)Google Scholar
  10. Ahn, D., Kim, T., Hyun, S.J., Lee, D.: Inferring user interest using familiarity and topic similarity with social neighbors in Facebook. In: Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, WI-IAT ’12, Washington, DC, USA, vol. 01, pp. 196–200. IEEE Computer Society (2012)Google Scholar
  11. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: The Semantic Web: 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, Busan, Korea, pp. 722–735. Springer (2007)Google Scholar
  12. Bellogn, A., Said, A.: Rate CTRCT, gain DCGDC, error MAEMA, precision MAPMA, learning MLM, error RRMS. Recommender systems evaluation. (2017). http://ir.ii.uam.es/~alejandro/2017/esnam.pdf. Accessed 10 June 2018
  13. Besel, C., Schlötterer, J., Granitzer, M.: Inferring semantic interest profiles from Twitter followees: does Twitter know better than your friends? In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, SAC ’16, New York, NY, USA, pp. 1152–1157. ACM (2016a)Google Scholar
  14. Besel, C., Schlötterer, J., Granitzer, M.: On the quality of semantic interest profiles for online social network consumers. ACM SIGAPP Appl. Comput. Rev. 16(3), 5–14 (2016b)CrossRefGoogle Scholar
  15. Bhargava, P., Brdiczka, O., Roberts, M.: Unsupervised modeling of users’ interests from their Facebook profiles and activities. In: Proceedings of the 20th International Conference on Intelligent User Interfaces, IUI ’15, New York, NY, USA, pp. 191–201. ACM (2015).  https://doi.org/10.1145/2678025.2701365
  16. Bhattacharya, P., Zafar, M.B., Ganguly, N., Ghosh, S., Gummadi, K.P.: Inferring user interests in the Twitter social network. In: Proceedings of the 8th ACM Conference on Recommender Systems, RecSys’14, New York, NY, USA, pp. 357–360. ACM (2014)Google Scholar
  17. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)zbMATHGoogle Scholar
  18. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)Google Scholar
  19. Bontcheva, K., Rout, D.: Making sense of social media streams through semantics: a survey. Semant. Web 5(5), 373–403 (2014).  https://doi.org/10.3233/SW-130110 Google Scholar
  20. Brickley, D., Miller, L.: FOAF vocabulary specification 0.98. (2012). http://xmlns.com/foaf/spec/. Accessed 10 Dec 2017
  21. Brusilovsky, P., Karagiannidis, C., Sampson, D.: The benefits of layered evaluation of adaptive applications and services. In: Empirical Evaluation of Adaptive Systems. Proceedings of Workshop at the Eighth International Conference on User Modeling, UM2001, pp. 1–8 (2001)Google Scholar
  22. Brusilovsky, P., Kobsa, A., Nejdl, W.: The Adaptive Web: Methods and Strategies of Web Personalization, vol. 4321. Springer, Berlin (2007)CrossRefGoogle Scholar
  23. Budak, C., Kannan, A., Agrawal, R., Pedersen, J.: Inferring user interests from microblogs. Technical report, Microsoft (2014)Google Scholar
  24. Carmagnola, F., Cena, F., Console, L., Cortassa, O., Gena, C., Goy, A., Torre, I., Toso, A., Vernero, F.: Tag-based user modeling for social multi-device adaptive guides. User Model. User Adapt. Interact. 18(5), 497–538 (2008).  https://doi.org/10.1007/s11257-008-9052-2 CrossRefGoogle Scholar
  25. Carmagnola, F., Cena, F., Gena, C.: User model interoperability: a survey. User Model. User Adapt. Interact. 21(3), 285–331 (2011).  https://doi.org/10.1007/s11257-011-9097-5 CrossRefGoogle Scholar
  26. Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E.: Short and tweet: experiments on recommending content from information streams. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, GA, USA, pp. 1185–1194. ACM (2010)Google Scholar
  27. Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative personalized tweet recommendation. In: SIGIR ’12: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, Portland, Oregon, USA, pp. 661–670. ACM (2012)Google Scholar
  28. Cohen, P.R., Perrault, C.R.: Elements of a plan-based theory of speech acts. Cogn. Sci. 3(3), 177–212 (1979)CrossRefGoogle Scholar
  29. Collins, A.M., Loftus, E.F.: A spreading-activation theory of semantic processing. Psychol. Rev. 82(6), 407 (1975)CrossRefGoogle Scholar
  30. Edmonds, J.: Optimum branchings. In: Dantzig, G.B., Veinott, A.F. (eds.) Mathematics and the Decision Sciences, pp. 335–345. American Mathematical Society, Providence (1968)Google Scholar
  31. Faralli, S., Stilo, G., Velardi, P.: Large scale homophily analysis in Twitter using a Twixonomy. In: Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina, pp. 2334–2340. AAAI Press (2015a)Google Scholar
  32. Faralli, S., Stilo, G., Velardi, P.: Recommendation of microblog users based on hierarchical interest profiles. Soc. Netw. Anal. Min. 5(1), 1–23 (2015b)CrossRefGoogle Scholar
  33. Faralli, S., Stilo, G., Velardi, P.: Automatic acquisition of a taxonomy of microblogs users’ interests. Web Semant. Sci. Serv. Agents. World Wide Web (2017).  https://doi.org/10.1016/j.websem.2017.05.004
  34. Färber, M., Ell, B., Menne, C., Rettinger, A.: A comparative survey of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Semant. Web J. 1, 1–26 (2015)Google Scholar
  35. Flati, T., Vannella, D., Pasini, T., Navigli, R.: Two is bigger (and better) than one: the Wikipedia bitaxonomy project. In: 52nd Annual Meeting of the Association for Computational Linguistics, ACL, Baltimore, MD, USA, pp. 945–955. Association for Computational Linguistics (ACL) (2014)Google Scholar
  36. Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 1606–1611. Morgan Kaufmann (2007)Google Scholar
  37. Gao, Q., Abel, F., Houben, G.J., Tao, K.: Interweaving trend and user modeling for personalized news recommendation. In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, WI-IAT ’11, Washington, DC, USA, vol. 01, pp. 100–103. IEEE Computer Society (2011)Google Scholar
  38. Gao, Q., Abel, F., Houben, G.J.: Genius: generic user modeling library for the social semantic web. In: The Semantic Web, pp. 160–175. Springer (2012)Google Scholar
  39. Garcia Esparza, S., O’Mahony, M.P., Smyth, B.: CatStream: categorising tweets for user profiling and stream filtering. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, IUI ’13, New York, NY, USA, pp. 25–36. ACM (2013)Google Scholar
  40. Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: The Adaptive Web, pp. 54–89. Springer, Berlin (2007)Google Scholar
  41. Gena, C., Weibelzahl, S.: Usability engineering for the adaptive web. In: The Adaptive Web, pp. 720–762. Springer (2007)Google Scholar
  42. Gong, W., Lim, E.P., Zhu, F.: Characterizing silent users in social media communities. In: ICWSM (2015)Google Scholar
  43. Große-Bölting, G., Nishioka, C., Scherp, A.: Generic process for extracting user profiles from social media using hierarchical knowledge bases. In: 2015 IEEE International Conference on Semantic Computing (ICSC) (2015).  https://doi.org/10.1109/ICOSC.2015.7050806
  44. Guha, R., Gupta, V., Raghunathan, V., Srikant, R.: User modeling for a personal assistant. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining—WSDM ’15, New York, NY, USA, pp. 275–284. ACM Press (2015)Google Scholar
  45. Haewoon, K., Changhyun, L., Hosung, P., Sue, M.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA. ACM (2010)Google Scholar
  46. Han, J., Pei, J.: Mining frequent patterns by pattern-growth: methodology and implications. ACM SIGKDD Explor. Newslett. 2(2), 14–20 (2000)MathSciNetCrossRefGoogle Scholar
  47. Han, L., Kashyap, A.L., Finin, T., Mayfield, J., Weese, J.: UMBC\_EBIQUITY-CORE: semantic textual similarity systems. In: The Second Joint Conference on Lexical and Computational Semantics, Atlanta, GA, USA, pp. 44–52. Association for Computational Linguistics (2013)Google Scholar
  48. Hannon, J., McCarthy, K., O’Mahony, M.P., Smyth, B.: A multi-faceted user model for Twitter. In: User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012. Montreal, Canada, pp. 303–309. Springer (2012)Google Scholar
  49. Heath, T., Bizer, C.: Linked data: evolving the web into a global data space. Synthesis lectures on the semantic web: theory and technology, vol. 1, no. 1, pp. 1–136 (2011)Google Scholar
  50. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004).  https://doi.org/10.1145/963770.963772 CrossRefGoogle Scholar
  51. Holden, S., Kay, J.: The Scrutable User Model and Beyond. Basser Department of Computer Science, University of Sydney, Sydney (1999)Google Scholar
  52. Hong, L., Doumith, A.S., Davison, B.D.: Co-factorization machines: modeling user interests and predicting individual decisions in Twitter. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM ’13, New York, NY, USA, pp. 557–566. ACM (2013)Google Scholar
  53. Hung, C.C., Huang, Y.C., Hsu, J.Y.j., Wu, D.K.C.: Tag-based user profiling for social media recommendation. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, pp. 151–156 (2008)Google Scholar
  54. Ingwersen, P.: Polyrepresentation of information needs and semantic entities elements of a cognitive theory for information retrieval interaction. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, pp. 101–110. Springer (1994)Google Scholar
  55. Java, A., Song, X., Finin, T., Tseng, B.: Why we Twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, San Jose, CA, USA, pp. 56–65. ACM (2007)Google Scholar
  56. Jiang, B., Sha, Y.: Modeling temporal dynamics of user interests in online social networks. Proc. Comput. Sci. 51, 503–512 (2015)CrossRefGoogle Scholar
  57. Jipmo, C.N., Quercini, G., Bennacer, N.: FRISK: a multilingual approach to find twitteR InterestS via wiKipedia BT. In: Advanced Data Mining and Applications: 13th International Conference, ADMA 2017, Singapore, Nov 2017, Proceedings, pp. 243–256. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69179-4_17
  58. Kaminskas, M., Bridge, D.: Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7(1), 2:1–2:42 (2016).  https://doi.org/10.1145/2926720 CrossRefGoogle Scholar
  59. Kang, J., Lee, H.: Modeling user interest in social media using news media and Wikipedia. Inf. Syst. 65, 52–64 (2016)CrossRefGoogle Scholar
  60. Kanta, M., Simko, M., Bieliková, M.: Trend-aware user modeling with location-aware trends on Twitter. In Proceedings of 7th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP (2012)Google Scholar
  61. Kapanipathi, P., Orlandi, F., Sheth, A., Passant, A.: Personalized filtering of the Twitter stream. In: Proceedings of the Second International Conference on Semantic Personalized Information Management: Retrieval and Recommendation, vol. 781, pp. 6–13. CEUR-WS.org, Bonn, Germany (2011)Google Scholar
  62. Kapanipathi, P., Jain, P., Venkataramani, C., Sheth, A.: User interests identification on Twitter using a hierarchical knowledge base. In: The Semantic Web: Trends and Challenges, Anissaras, Crete, Greece, pp. 99–113. Springer (2014)Google Scholar
  63. Karatay, D., Karagoz, P.: User interest modeling in Twitter with named entity recognition. In: Making Sense of Microposts (# Microposts 2015), Florence, Italy, pp. 17–20 (2015)Google Scholar
  64. Kay, J.: Scrutable adaptation: because we can and must. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 11–19. Springer (2006)Google Scholar
  65. Kim, D., Jo, Y., Moon, I.C., Oh, A.: Analysis of Twitter lists as a potential source for discovering latent characteristics of users. In: ACM CHI Workshop on Microblogging, Atlanta, GA, USA, p. 4. Citeseer (2010)Google Scholar
  66. Kitchenham, B.: Procedures for Performing Systematic Reviews, vol. 33, pp. 1–26. Keele University, Keele (2004)Google Scholar
  67. Liao, Y., Moshtaghi, M., Han, B., Karunasekera, S., Kotagiri, R., Baldwin, T., Harwood, A., Pattison, P.: Mining micro-blogs: opportunities and challenges. In: Abraham, A. (ed.) Computational Social Networks, pp. 129–159. Springer, Berlin (2012)CrossRefGoogle Scholar
  68. Lim, K.H., Datta, A.: Interest classification of twitter users using Wikipedia. In: Proceedings of the 9th International Symposium on Open Collaboration, WikiSym ’13, Hong Kong, China, pp. 22:1—22:2. ACM (2013)Google Scholar
  69. Liu, J., Zhang, F., Song, X., Song, Y.I., Lin, C.Y., Hon, H.W.: What’s in a name? An unsupervised approach to link users across communities. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, Rome, Italy, pp. 495–504. ACM (2013)Google Scholar
  70. Lu, C., Lam, W., Zhang, Y.: Twitter user modeling and tweets recommendation based on Wikipedia concept graph. In: Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, ON, Canada (2012)Google Scholar
  71. Manrique, R., Mariño, O.: How does the size of a document affect linked open data user modeling strategies? In: Proceedings of the International Conference on Web Intelligence, WI ’17, New York, NY, USA, pp. 1246–1252. ACM (2017)Google Scholar
  72. Mezghani, M., Zayani, C.A., Amous, I., Gargouri, F.: A user profile modelling using social annotations: a survey. In: Proceedings of the 21st International Conference on World Wide Web, WWW ’12 Companion, New York, NY, USA, pp. 969–976. ACM (2012)Google Scholar
  73. Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on Twitter: a first look. In: Proceedings of the 4th Workshop on Analytics for Noisy Unstructured Text Data, Toronto, ON, Canada, pp. 73–80. ACM (2010)Google Scholar
  74. Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Lin, D., Wu, D. (eds.) Proceedings of EMNLP 2004, Barcelona, Spain, pp. 404–411. Association for Computational Linguistics (2004)Google Scholar
  75. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  76. Myers, S.A., Leskovec, J.: The bursty dynamics of the Twitter information network. In: Proceedings of the 23rd International Conference on World Wide Web, Seoul, Korea, pp. 913–924. ACM (2014)Google Scholar
  77. Narducci, F., Musto, C., Semeraro, G., Lops, P., Gemmis, M.: Leveraging encyclopedic knowledge for transparent and serendipitous user profiles. In: User Modeling, Adaptation, and Personalization: 21st International Conference, pp. 350–352. Springer, Berlin (2013)Google Scholar
  78. Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  79. Nechaev, Y., Corcoglioniti, F., Giuliano, C.: Concealing interests of passive users in social media. In: The Re-coding Black Mirror 2017 Workshop Co-located with 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria (2017)Google Scholar
  80. Nguyen, P.T., Tomeo, P., Di Noia, T., Di Sciascio, E.: Content-based recommendations via DBpedia and Freebase: a case study in the music domain. In: International Semantic Web Conference, pp. 605–621 (2015)Google Scholar
  81. Nishioka, C., Scherp, A.: Profiling vs. time vs. content: what does matter for top-k publication recommendation based on Twitter profiles? In: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL ’16, New York, NY, USA, pp. 171–180. ACM (2016)Google Scholar
  82. Nishioka, C., Große-Bölting, G., Scherp, A.: Influence of time on user profiling and recommending researchers in social media. In: Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business, i-KNOW ’15, New York, NY, USA, pp. 9:1–9:8. ACM (2015)Google Scholar
  83. O’Banion, S., Birnbaum, L., Hammond, K.: Social media-driven news personalization. In: Proceedings of the 4th ACM RecSys Workshop on Recommender Systems and the Social Web, Dublin, Ireland, pp. 45–52. ACM (2012)Google Scholar
  84. Orlandi, F., Breslin, J., Passant, A.: Aggregated, interoperable and multi-domain user profiles for the social web. In: Proceedings of the 8th International Conference on Semantic Systems, Graz, Austria, pp. 41–48. ACM (2012)Google Scholar
  85. Orlandi, F., Kapanipathi, P., Sheth, A., Passant, A.: Characterising concepts of interest leveraging linked data and the social web. In: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), WI-IAT ’13, Washington, DC, USA, vol. 01, pp. 519–526. IEEE Computer Society (2013)Google Scholar
  86. Paramythis, A., Weibelzahl, S., Masthoff, J.: Layered evaluation of interactive adaptive systems: framework and formative methods. User Model. User Adapt. Interact. 20(5), 383–453 (2010)CrossRefGoogle Scholar
  87. Peñas, P., del Hoyo, R., Vea-Murguía, J., González, C., Mayo, S.: Collective knowledge ontology user profiling for Twitter—automatic user profiling. In: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) (2013)Google Scholar
  88. Perera, S., Mendes, P.N., Alex, A., Sheth, A.P., Thirunarayan, K.: Implicit entity linking in tweets BT—the semantic web. In: Sack, H., Blomqvist, E., D’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) Latest Advances and New Domains: 13th International Conference, ESWC 2016, pp. 118–132. Springer, Cham (2016)Google Scholar
  89. Perrault, C.R., Allen, J.F., Cohen, P.R.: Speech acts as a basis for understanding dialogue coherence. In: Proceedings of the 1978 Workshop on Theoretical Issues in Natural Language Processing, pp. 125–132. Association for Computational Linguistics (1978)Google Scholar
  90. Phelan, O., McCarthy, K., Smyth, B.: Using Twitter to recommend real-time topical news. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys ’09, New York, NY, USA, pp. 385–388. ACM (2009)Google Scholar
  91. Piao, G., Breslin, J.J.G.: Analyzing aggregated semantics-enabled user modeling on Google+ and Twitter for personalized link recommendations. In: UMAP 2016—Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, Halifax, NS, Canada, pp. 105–109. ACM (2016a)  https://doi.org/10.1145/2930238.2930278
  92. Piao, G., Breslin, J.J.G.: Exploring dynamics and semantics of user interests for user modeling on Twitter for link recommendations. In: Proceedings of the 12th International Conference on Semantic Systems, Leipzig, Germany, 13–14 Sept 2016, pp. 81–88. ACM (2016b).  https://doi.org/10.1145/2993318.2993332
  93. Piao, G., Breslin, J.J.G.: Interest representation, enrichment, dynamics, and propagation: a study of the synergetic effect of different user modeling dimensions for personalized recommendations on Twitter. In: LNAI, Bologna, Italy, vol. 10024. Springer (2016c).  https://doi.org/10.1007/978-3-319-49004-5_32
  94. Piao, G., Breslin, J.J.G.: User modeling on Twitter with WordNet Synsets and DBpedia concepts for personalized recommendations. In: International Conference on Information and Knowledge Management, Proceedings, Indianapolis, IN, USA, 24–28 Oct 2016, pp. 2057–2060. ACM (2016d).  https://doi.org/10.1145/2983323.2983908
  95. Piao, G., Breslin, J.J.G.: Inferring user interests for passive users on Twitter by leveraging followee biographies. In: LNCS, Aberdeen, UK, vol. 10193. Springer (2017a).  https://doi.org/10.1007/978-3-319-56608-5_10
  96. Piao, G., Breslin, J.J.G.: Leveraging followee list memberships for inferring user interests for passive users on Twitter. In: HT 2017—Proceedings of the 28th ACM Conference on Hypertext and Social Media, Prague, Czech Republic. ACM Press (2017b).  https://doi.org/10.1145/3078714.3078730
  97. Rich, E.: User modeling via stereotypes. Cogn. Sci. 3(4), 329–354 (1979)CrossRefGoogle Scholar
  98. Ritter, A., Clark, S., Etzioni, O.: Named entity recognition in tweets: an experimental study. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Edinburgh, UK, pp. 1524–1534. Association for Computational Linguistics (2011)Google Scholar
  99. Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, USA, UAI ’04, Arlington, VA, pp. 487–494. AUAI Press (2004)Google Scholar
  100. Rotta, R., Noack, A.: Multilevel local search algorithms for modularity clustering. J. Exp. Algorithmics 16, 2.3:2.1–2.3:2.27 (2011).  https://doi.org/10.1145/1963190.1970376 MathSciNetCrossRefzbMATHGoogle Scholar
  101. Salton, G., McGill, M.J.: Introduction to Modern information Retrieval. McGraw-Hill, New York (1986)zbMATHGoogle Scholar
  102. Sang, J., Lu, D., Xu, C.: A probabilistic framework for temporal user modeling on microblogs. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM ’15, New York, NY, USA, pp. 961–970. ACM (2015).  https://doi.org/10.1145/2806416.2806470
  103. Shen, W., Wang, J., Luo, P., Wang, M.: Linking named entities in tweets with knowledge base via user interest modeling. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’13, New York, NY, USA, pp. 68–76. ACM (2013).  https://doi.org/10.1145/2487575.2487686
  104. Sheth, A., Kapanipathi, P.: Semantic filtering for social data. IEEE Internet Comput. 20(4), 74–78 (2016)CrossRefGoogle Scholar
  105. Siehndel, P., Kawase, R.: TwikiMe!: user profiles that make sense. In: Proceedings of the 2012th International Conference on Semantic Web (Posters and Demonstrations Track), ISWC-PD’12, vol. 914, pp. 61–64. CEUR-WS.org (2012)Google Scholar
  106. Spasojevic, N., Yan, J., Rao, A., Bhattacharyya, P.: LASTA: large scale topic assignment on multiple social networks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, New York, NY, USA, pp. 1809–1818. ACM (2014).  https://doi.org/10.1145/2623330.2623350
  107. Stefani, A.: Personalizing access to web sites: the SiteIF project. In: Proceedings of the 2nd Workshop on Adaptive Hypertext and Hypermedia HYPERTEXT (1998)Google Scholar
  108. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)Google Scholar
  109. Szomszor, M., Alani, H., Cantador, I., O’Hara, K., Shadbolt, N.: Semantic modelling of user interests based on cross-folksonomy analysis. In: The Semantic Web—ISWC 2008, Lecture Notes in Computer Science, SE-40, vol. 5318, pp. 632–648. Springer, Berlin (2008)Google Scholar
  110. Tao, K., Abel, F., Gao, Q., Houben, G.J.: TUMS: Twitter-based user modeling service. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.), The Semantic Web: ESWC 2011 Workshops, vol. 7117, chap. 22, pp. 269–283. Springer, Berlin (2012)Google Scholar
  111. Tommaso, G.D., Faralli, S., Stilo, G., Velardi, P.: Wiki-MID: a very large multi-domain interests dataset of Twitter users with mappings to Wikipedia. In: The 17th International Semantic Web Conference. Springer (2018)Google Scholar
  112. Trikha, A.K., Zarrinkalam, F., Bagheri, E.: Topic-association mining for user interest detection. In: The 40th European Conference on Information Retrieval. Springer (2018)Google Scholar
  113. Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)CrossRefGoogle Scholar
  114. Vu, T., Perez, V.: Interest mining from user tweets. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, CIKM ’13, New York, NY, USA, pp. 1869–1872. ACM (2013)Google Scholar
  115. Weng, J., Lim, E.P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential Twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM ’10, New York, NY, USA, pp. 261–270. ACM (2010)Google Scholar
  116. White, R.W., Bailey, P., Chen, L.: Predicting user interests from contextual information. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’09, New York, NY, USA, pp. 363–370. ACM (2009)Google Scholar
  117. Xu, Z., Ru, L., Xiang, L., Yang, Q.: Discovering user interest on Twitter with a modified author-topic model. In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 01, Washington, DC, USA, pp. 422–429. IEEE Computer Society (2011)Google Scholar
  118. Zarrinkalam, F.: Semantics-enabled user interest mining. In: Gandon, F., Sabou, M., Sack, H., D’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) The Semantic Web. Latest Advances and New Domains, SE-54, Lecture Notes in Computer Science, vol. 9088, pp. 817–828. Springer (2015)Google Scholar
  119. Zarrinkalam, F., Kahani, M.: Semantics-enabled user interest detection from Twitter. In: 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Singapore, pp. 469–476 (2015)Google Scholar
  120. Zarrinkalam, F., Fani, H., Bagheri, E., Kahani, M.: Inferring implicit topical interests on Twitter. In: European Conference on Information Retrieval, pp. 479–491, Padua, Italy. Springer (2016)Google Scholar
  121. Zarrinkalam, F., Fani, H., Bagheri, E., Kahani, M.: Predicting users’ future interests on Twitter. In: European Conference on Information Retrieval, pp. 464–476. Springer (2017)Google Scholar
  122. Zhou, X., Xu, Y., Li, Y., Josang, A., Cox, C.: The state-of-the-art in personalized recommender systems for social networking. Artif. Intell. Rev. 37(2), 119–132 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Insight Centre for Data Analytics, Data Science InstituteNational University of Ireland GalwayGalwayIreland

Personalised recommendations