Skip to main content

Advertisement

SpringerLink
Log in

Profiling users via their reviews: an extended systematic mapping study

  • Special Section Paper
  • Published:
Software and Systems Modeling Aims and scope Submit manuscript

Abstract

With the extensive development of big data and social networks, the user profile field has received much attention. User profiling is essential for understanding the characteristics of various users, contributing to better understanding of their requirements in specific scenarios. User-generated contents which directly reflect people’s thoughts and intention are a valuable source for profiling users, among which user reviews by nature are invaluable sources for acquiring user requirements and have drawn increasing attention from both academia and industry. However, review-based user profiling (RBUP), as an emerging research direction, has not been systematically reviewed, hindering researchers from further investigation. In this work, we carry out a systematic mapping study on review-based user profiling, with an emphasis on investigating the generic analysis process of RBUP and identifying potential research directions. Specifically, 51 out of 2478 papers were carefully selected for investigation under a standardized and systematic procedure. By carrying out in-depth analysis over such papers, we have identified a generic process that should be followed to perform review-based user profiling. In addition, we perform multi-dimensional analysis on each step of the process in order to review current research progress and identify challenges and potential research directions. The results show that although traditional methods have been continuously improved, they are not sufficient to unleash the full potential of large-scale user reviews, especially the use of heterogeneous data for multi-dimensional user profiling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Notes

  1. http://ieeexplore.ieee.org.

  2. https://www.webofknowledge.com.

  3. http://www.scopus.com.

  4. http://dl.acm.org.

  5. https://www.dropbox.com/sh/v63qqelddxg1lim/AABjLszZekUOtDiKV8q8aOROa?dl=0.

References

  1. Ahn, H.J., Kang, H., Lee, J.: Selecting a small number of products for effective user profiling in collaborative filtering. Expert Syst. Appl. 37(4), 3055–3062 (2010). https://doi.org/10.1016/j.eswa.2009.09.025

    Article  Google Scholar 

  2. Ampatzoglou, A., Bibi, S., Avgeriou, P., Verbeek, M., Chatzigeorgiou, A.: Identifying, categorizing and mitigating threats to validity in software engineering secondary studies. Inf. Softw. Technol. 106, 201–230 (2019). https://doi.org/10.1016/j.infsof.2018.10.006

    Article  Google Scholar 

  3. Ananthapadmanaban, K.R., Srivatsa, S.K.: Personalization of user profile: creating user profile ontology for tamilnadu tourism. Int. J. Comput. Appl. 23(8), 42–47 (2011)

    Google Scholar 

  4. Budgen, D., Kitchenham, B., Charters, S., Turner, M., Brereton, P., Linkman, S.: Preliminary results of a study of the completeness and clarity of structured abstracts. In: International Conference on Evaluation & Assessment in Software Engineering (2007)

  5. Catherine, R., Cohen, W.: Personalized recommendations using knowledge graphs: a probabilistic logic programming approach. In: ACM Conference on Recommender Systems (2016)

  6. Chen, W., Zheng, H.T., Mao, X.X.: Extracting deep semantic information for intelligent recommendation. In: International Conference on Neural Information Processing, pp. 134–144. Springer (2017)

  7. Cicchetti, A., Ciccozzi, F., Pierantonio, A.: Multi-view approaches for software and system modelling: a systematic literature review. Softw. Syst. Model. 18, 3207 (2019)

    Article  Google Scholar 

  8. Cufoglu, A.: User profiling-a short review. Int. J. Comput. Appl. https://doi.org/10.5120/18888-0179 (2014)

  9. Dong, R., Smyth, B.: From more-like-this to better-than-this: hotel recommendations from user generated reviews. In: UMAP16, Halifax, NS, Canada. ACM (2016)

  10. Dong, X., Li, T., Li, X., Song, R., Ding, Z.: Review-based user profiling: a systematic mapping study Enterprise Business-Process and Information Systems Modeling, pp. 229–244. Springer, Cham (2019)

  11. Hojaji, F., Mayerhofer, T., Zamani, B., Hamou-Lhadj, A., Bousse, E.: Model execution tracing: a systematic mapping study. Softw. Syst. Model. 18, 3461 (2019)

    Article  Google Scholar 

  12. Horkoff, J., Aydemir, F.B., Cardoso, E., Li, T., Maté, A., Paja, E., Salnitri, M., Piras, L., Mylopoulos, J., Giorgini, P.: Goal-oriented requirements engineering: an extended systematic mapping study. Requir. Eng. 24(2), 133–160 (2017). https://doi.org/10.1007/s00766-017-0280-z

    Article  Google Scholar 

  13. Jiang, M., Song, D., Liao, L., Zhu, F.: A bayesian recommender model for user rating and review profiling. Tsinghua Sci. Technol. 20(6), 634–643 (2015)

    Article  Google Scholar 

  14. Kai, P., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: International Conference on Evaluation & Assessment in Software Engineering (2008)

  15. Kanoje, S., Girase, S., Mukhopadhyay, D.: User profiling trends, techniques and applications. Int. J. Adv. Found. Res. Comput. 1, 2348–4853 (2014)

    Google Scholar 

  16. Keele, S., et al.: Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, Ver. 2.3 EBSE Technical Report. EBSE (2007)

  17. Kitchenham, B.A., Budgen, D., Brereton, O.P.: Using mapping studies as the basis for further research: a participant-observer case study. Inf. Softw. Technol. 53(6), 638–651 (2011)

    Article  Google Scholar 

  18. Kitchenham, B.A., Pfleeger, S.L., Pickard, L.M., Jones, P.W., Hoaglin, D.C., Emam, K.E., Rosenberg, J.: Preliminary guidelines for empirical research in software engineering. IEEE Trans. Softw. Eng. 28(8), 721–734 (2002)

    Article  Google Scholar 

  19. Lancaster, G.A., Susanna, D., Williamson, P.R.: Design and analysis of pilot studies: recommendations for good practice. J. Eval. Clin. Pract. 10(2), 307–312 (2010)

    Article  Google Scholar 

  20. Levi, A., Mokryn, O., Diot, C., Taft, N.: Finding a needle in a haystack of reviews: cold start context-based hotel recommender system. In: ACM Conference on Recommender Systems (2012)

  21. Li, C., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Model. User-Adap. Inter. 25(2), 99–154 (2015)

    Article  Google Scholar 

  22. Luo, Y.: The comparison of personalization recommendation for e-commerce. Phys. Proc. 25, 475–478 (2012)

    Article  Google Scholar 

  23. Markov, I., Gómez-Adorno, H., Posadas-Durán, J.P., Sidorov, G., Gelbukh, A.: Author profiling with doc2vec neural network-based document embeddings. In: Mexican International Conference on Artificial Intelligence, pp. 117–131. Springer (2016)

  24. Moon, C., Jones, P., Samatova, N.F.: Learning entity type embeddings for knowledge graph completion. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17, pp. 2215–2218. ACM, New York, NY, USA (2017)

  25. Natarajan, S., Moh, M.: Recommending news based on hybrid user profile, popularity, trends, and location. In: 2016 International Conference on Collaboration Technologies and Systems (CTS), pp. 204–211 (2016). Doi: https://doi.org/10.1109/CTS.2016.0050

  26. Papadimitriou, C.H., Raghavan, P., Tamaki, H., Vempala, S.: Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61(2), 217–235 (2000)

    Article  MathSciNet  Google Scholar 

  27. Park, D.H., Kim, H.K., Choi, I.Y., Kim, J.K.: A literature review and classification of recommender systems research. Int. Proc. Econ. Dev. Res. 39(11), 10059–10072 (2012)

    Google Scholar 

  28. Peng, J., Detchon, S., Choo, K.K.R., Ashman, H.: Astroturfing detection in social media: a binary n-gram-based approach. Concur. Comput. Pract. Exp. 29(17), e4013 (2017)

    Article  Google Scholar 

  29. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. In: ACM Sigkdd International Conference on Knowledge Discovery & Data Mining (2014)

  30. Ramkumar, V., Rajasekar, S., Swamynathan, S.: Scoring products from reviews through application of fuzzy techniques. Expert Syst. Appl. 37(10), 6862–6867 (2010)

    Article  Google Scholar 

  31. Sarwat, M., Levandoski, J.J., Eldawy, A., Mokbel, M.F.: Lars*: An efficient and scalable location-aware recommender system. IEEE Trans. Knowl. Data Eng. 26(6), 1384–1399 (2014)

    Article  Google Scholar 

  32. Stewart, S., Davies, J.: User profiling techniques : a critical review. In: 19th Annual BCS-IRSG Colloquium on IR Aberdeen, UK. 8th-9th April 1997 (1997)

  33. Suganeshwari, G., Ibrahim, S.: A survey on collaborative filtering based recommendation system. In: Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges, vol. 49, 503–518 (2016). https://doi.org/10.1007/978-3-319-30348-242

  34. Szvetits, M., Zdun, U.: Systematic literature review of the objectives, techniques, kinds, and architectures of models at runtime. Softw. Syst. Model. 15(1), 31–69 (2016)

    Article  Google Scholar 

  35. Wang, D., Peng, C., Zhu, W.: Structural deep network embedding. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2016)

  36. Williams, G., Mahmoud, A.: Modeling user concerns in the app store: A case study on the rise and fall of yik yak. In: 2018 IEEE 26th International Requirements Engineering Conference (RE), pp. 64–75 (2018)

  37. Wohlin, C.: Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: International Conference on Evaluation & Assessment in Software Engineering (2014)

  38. Wolny, S., Mazak, A., Carpella, C., Geist, V., Wimmer, M.: Thirteen years of sysml: a systematic mapping study. Softw. Syst. Model. (2019). https://doi.org/10.1007/s10270-019-00735-y

    Article  Google Scholar 

  39. Wu, S., Guo, W., Xu, S., Huang, Y., Wang, L., Tan, T.: Coupled topic model for collaborative filtering with user-generated content. IEEE Trans. Hum. Mach. Syst. 46(6), 908–920 (2016)

    Article  Google Scholar 

  40. Wu, S.L.: Enabling searching of user ratings and reviews using user profile location, and social networks (2011). US Patent 7,895,177

  41. Xue, B., Fu, C., Shaobin, Z.: A study on sentiment computing and classification of sina weibo with word2vec. In: 2014 IEEE International Congress on Big Data, pp. 358–363. IEEE (2014)

  42. Yang, Z., Zhi, L., Zhi, J., Chen, Y.: A systematic literature review of requirements modeling and analysis for self-adaptive systems. In: International Working Conference on Requirements Engineering: Foundation for Software Quality (2014)

  43. Zhong, Y., Yuan, N.J., Zhong, W., Zhang, F., Xie, X.: You are where you go: Inferring demographic attributes from location check-ins. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 295–304. ACM (2015)

Download references

Acknowledgements

This work is supported by the National Key R&D Program of China (Nos. 2017YFC0803300, 2017YFC0803307) and the National Natural Science of Foundation of China (Nos. 61902010, 91546111, 91646201).

Author information

Authors and Affiliations

  1. Beijing University of Technology, Beijing, People’s Republic of China

    Xin Dong, Tong Li, Rui Song & Zhiming Ding

Authors
  1. Xin Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rui Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiming Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tong Li.

Additional information

Communicated by Jelena Zdravkovic and Iris Reinhartz-Berger.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A: RBUP papers

Appendix A: RBUP papers

  1. (S1)

    Jiang M., Song D., Liao L., Zhu F.: A Bayesian recommender model for user rating and review profiling. Tsinghua Science and Technology. 2015).

  2. (S2)

    Eskandanian F., Mobasher B., Burke R.: A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems. In: 2017)

  3. (S3)

    Cardiff J., Cardiff J., Rosso P.: A comparative evaluation of personality estimation algorithms for the twin recommender system. In: International Workshop on Search & Mining User-generated Contents, vol. 2011)

  4. (S4)

    Missaoui S.: A Language Modeling Approach for the Recommendation of Tourism-Related Services. In: Acm Sigapp Symposium on Applied Computing Sac, vol. 2017)

  5. (S5)

    Gao Y., Yu W., Chao P., Rong Z., Zhou A., Yang X.: A Restaurant Recommendation System by Analyzing Ratings and Aspects in Reviews. 2015).

  6. (S6)

    Abdullah N., Yue X., Geva S.: A Recommender System for Infrequent Purchased Products based on User Navigation and Product Review Data. In: Web Information Systems Engineering-wise Workshops-wise International Symposium Wiss, vol. 2010)

  7. (S7)

    Roshchina A., Cardiff J., Rosso P.: A User Profile Construction in the TWIN Personality-based Recommender System. 2011).

  8. (S8)

    Dragoni M.: A Three-Phase Approach for Exploiting Opinion Mining in Computational Advertising. IEEE Intelligent Systems. 32. 21 (2017).

  9. (S9)

    Sano M.: An Affect-Based Text Mining System for Qualitative Analysis of Japanese Free Text. In: 2004)

  10. (S10)

    Yang P., Hui F.: Opinion-based User Profile Modeling for Contextual Suggestions. In: Conference on the Theory of Information Retrieval, vol. 2013)

  11. (S11)

    Kardaras D.K., Kaperonis S., Barbounaki S., Petrounias I., Bithas K.: An Approach to Modelling User Interests Using TF-IDF and Fuzzy Sets Qualitative Comparative Analysis. 2018).

  12. (S12)

    Angioni M., Devola A., Locci M., Mura F., Tuveri F., Varchetta M.: An Evaluation Method for the Performance Measurement of an Opinion Mining System. 2018)

  13. (S13)

    Agreste S., Meo P.D., Ferrara E., Piccolo S., Provetti A.: Analysis of a Heterogeneous Social Network of Humans and Cultural Objects. IEEE Transactions on Systems Man & Cybernetics Systems. 45. 559 (2014).

  14. (S14)

    Sun L., Guo J., Zhu Y.: Applying uncertainty theory into the restaurant recommender system based on sentiment analysis of online Chinese reviews. World Wide Web-internet & Web Information Systems. 1 (2018).

  15. (S15)

    Jian P., Choo K.K., Ashman H.: Astroturfing Detection in Social Media: Using Binary n-Gram Analysis for Authorship Attribution. In: 2016 IEEE Trustcom/BigData SE/ISPA, vol. 2016)

  16. (S16)

    Musat C.C., Liang Y., Faltings B.: Recommendation using textual opinions. In: International Joint Conference on Artificial Intelligence, vol. 2013)

  17. (S17)

    Dragoni M.: Computational advertising in social networks: an opinion mining-based approach. In: 1798. ACM,2018)

  18. (S18)

    Rossetti M., Stella F., Zanker M.: Analyzing user reviews in tourism with topic models. Information Technology & Tourism. 16. 5 (2016).

  19. (S19)

    Dong R., Smyth B.: From More-Like-This to Better-Than-This: Hotel Recommendations from User Generated Reviews. 2016).

  20. (S20)

    Wu S., Guo W., Xu S., Huang Y., Wang L., Tan T.: Coupled Topic Model for Collaborative Filtering With User-Generated Content. IEEE Transactions on Human-Machine Systems. 46. 908 (2017).

  21. (S21)

    Tutubalina, E., Nikolenko, S.: Demographic prediction based on user reviews about medications. Computacion y Sistemas 21(2), 227C241 (2017)

  22. (S22)

    Tutubalina E., Nikolenko S.: Exploring convolutional neural networks and topic models for user profiling from drug reviews. Multimedia Tools & Applications. 1 (2017)

  23. (S23)

    Yang Y., Hu S., Yi C., Du Q., Leung H.F., Lau R.Y.: Exploring Reviews and Ratings on Reviews for Personalized Search. In: Revised Selected Papers of the Icwl International Workshops on Current Developments in Web Based Learning, vol. 2015)

  24. (S24)

    Chen W., Zheng H.T., Mao X.X.: Extracting Deep Semantic Information for Intelligent Recommendation. In: International Conference on Neural Information Processing, vol. 2017)

  25. (S25)

    Xu Y., Bing L.: Sentiment Classification Incorporating User Profile. In: International Conference on Information Science & Control Engineering, vol. 2017)

  26. (S26)

    Zhong Y., Yuan N.J., Zhong W., Zhang F., Xie X.: You Are Where You Go:Inferring Demographic Attributes from Location Check-ins. 2015).

  27. (S27)

    Abdillah O., Adriani M.: Mining User Interests through Internet Review Forum for Building Recommendation System. In: IEEE International Conference on Advanced Information Networking & Applications Workshops, vol. 2015)

  28. (S28)

    Abdullah N., Yue X., Geva S., Chen J.: Infrequent Purchased Product Recommendation Making Based on User Behaviour and Opinions in E-commerce Sites. In: IEEE International Conference on Data Mining Workshops, vol. 2011)

  29. (S29)

    Ramkumar V., Rajasekar S., Swamynathan S.: Scoring products from reviews through application of fuzzy techniques. Expert Systems with Applications. 37. 6862 (2010).

  30. (S30)

    Levi A., Mokryn O., Diot C., Taft N. Finding a needle in a haystack of reviews:cold start context-based hotel recommender system demo. Acm Conference on Recommender Systems. 2012.

  31. (S31)

    Cozza V., Petrocchi M., Spognardi A.: Mining implicit data association from Tripadvisor hotel reviews. 2018)

  32. (S32)

    Malmi E., Weber I.: You Are What Apps You Use: Demographic Prediction Based on User’s Apps. 2016).

  33. (S33)

    Zhang Y.: Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation. 2015).

  34. (S34)

    K. L., Y. C., C. S., M. L.: Leveraging Online Word of Mouth for Personalized App Recommendation. IEEE Transactions on Computational Social Systems. 5. 1061 (2018).

  35. (S35)

    Huiming, W. and L. Nianlong. Collaborative filtering enhanced by user free-text reviews topic modelling. in 2014 International Conference on Information and Communications Technologies (ICT 2014). 2014.

  36. (S36)

    Liu, H., et al., Combining user preferences and user opinions for accurate recommendation. Electronic Commerce Research and Applications, 2013. 12(1): p. 14–23.

  37. (S37)

    Yang, J. and B. Yecies, Mining Chinese social media UGC: a big-data framework for analyzing Douban movie reviews. Journal of Big Data, 2016. 3(1): p. 3.

  38. (S38)

    Korfiatis, N. and M. Poulos, Using online consumer reviews as a source for demographic recommendations: A case study using online travel reviews. Expert Systems with Applications, 2013. 40(14): p. 5507–5515.

  39. (S39)

    Pradhan, S. and V. Gay. Introducing Patient and Dentist Profiling and Crowdsourcing to Improve Trust in Dental Care Recommendation Systems. 2014. Berlin, Heidelberg: Springer Berlin Heidelberg.

  40. (S40)

    Zhao, W.X., et al., Exploring demographic information in social media for product recommendation. Knowledge and Information Systems, 2016. 49(1): p. 61–89.

  41. (S41)

    Ma, Y., G. Chen and Q. Wei, Finding users preferences from large-scale online reviews for personalized recommendation. Electronic Commerce Research, 2017. 17(1): p. 3–29.

  42. (S42)

    Cao, N., et al. Sentimental Preference Extraction from Online Reviews for Recommendation. 2015. Cham: Springer International Publishing.

  43. (S43)

    Chen, L. and F. Wang, Preference-based clustering reviews for augmenting e-commerce recommendation. Knowledge-Based Systems, 2013. 50: p. 44–59.

  44. (S44)

    Barragns-Martnez, A.B., et al., A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Information Sciences, 2010. 180(22): p. 4290–4311.

  45. (S45)

    S., M., et al., KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications. IEEE Transactions on Parallel and Distributed Systems, 2014. 25(12): p. 3221–3231.

  46. (S46)

    Decker, R. and M. Trusov, Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 2010. 27(4): p. 293–307.

  47. (S47)

    Peng, J., K.R. Choo and H. Ashman, Bit-level n-gram based forensic authorship analysis on social media: Identifying individuals from linguistic profiles. Journal of Network and Computer Applications, 2016. 70: p. 171–182.

  48. (S48)

    Peng, J., R.K.K. Choo and H. Ashman. Astroturfing detection in social media: Using binary n-gram analysis for authorship attribution. 2016: Institute of Electrical and Electronics Engineers Inc.

  49. (S49)

    S., A., J. S. and B. M. Comparison of Personalised Systems. in 2013 6th International Conference on Emerging Trends in Engineering and Technology. 2013.

  50. (S50)

    Chen, L., G. Chen and F. Wang, Recommender systems based on user reviews: the state of the art. User Modeling and User-Adapted Interaction, 2015. 25(2): p. 99–154.

  51. (S51)

    Suganeshwari, G. and S.P. Syed Ibrahim. A Survey on Collaborative Filtering Based Recommendation System. 2016. Cham: Springer International Publishing.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, X., Li, T., Song, R. et al. Profiling users via their reviews: an extended systematic mapping study. Softw Syst Model 20, 49–69 (2021). https://doi.org/10.1007/s10270-020-00790-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10270-020-00790-w

Keywords

Search

Navigation