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Sentiment aware tensor model for multi-criteria recommendation

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Abstract

With the advance of sentiment analysis techniques, several studies have been on Multi-Criteria Recommender Systems (MCRS) leveraging sentiment information. However, partial preferences quite and naturally happen in MCRS and negatively affect the predictive performances of sentiment analysis and multi-criteria recommendation. In this paper, we propose a Sentiment Aware Tensor Model-based MCRS named SATM. It maps between i) a set of multiple classes from explicit user feedbacks and ii) sentiments extracted from free texts in user reviews. In particular, we found the four patterns of the partial preferences and applied a rule-based function to detect them and fill their incomplete ratings intuitively. Lastly, we introduce a mapping function of the misinterpretable patterns into sentiment scores in order to generate virtual user preferences that construct the SATM. Experiments on three datasets (i.e., hotel and restaurant reviews) collected from TripAdvisor show that the SATM is superior to various baseline techniques, including state-of-the-art approaches. Additionally, the experimental evaluation of the SATM’s variants reveals that the rule-based and mapping functions can handle the partial preferences and improve the MCRS’ performance, regardless of target domains.

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Notes

  1. P-Tucker, https://github.com/sejoonoh/P-Tucker

  2. CoSTCo, https://github.com/USC-Melady/KDD19-CoSTCo

References

  1. Adomavicius G, Kwon Y (2007) New recommendation techniques for multicriteria rating systems. IEEE Intell Syst 22(3):48–55. https://doi.org/10.1109/MIS.2007.58

    Article  Google Scholar 

  2. Akbik A, Blythe D, Vollgraf R (2018) Contextual string embeddings for sequence labeling. In: Bender EM, Derczynski L, Isabelle P (eds) Proceedings of the 27th international conference on computational linguistics. Santa Fe, New Mexico, USA, pp 1638–1649

  3. Akbik A, Bergmann T, Blythe D, Rasul K, Schweter S, Vollgraf R (2019) FLAIR: An easy-to-use framework for state-of-the-art NLP. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: Human language technologies NAACL-HLT; Ammar W, Louis A, Mostafazadeh N. USA, Minneapolis, MN, pp 54–59. https://doi.org/10.18653/v1/n19-4010

  4. Al-Ghuribi SM, SAM N (2019) Multi-criteria review-based recommender system-the state of the art. IEEE Access 7:169446–169468. https://doi.org/10.1109/ACCESS.2019.2954861

    Article  Google Scholar 

  5. Alhijawi B, Kilani Y (2020) A collaborative filtering recommender system using genetic algorithm. Information Processing & Management 57(6):102310. https://doi.org/10.1016/j.ipm.2020.102310

    Article  Google Scholar 

  6. Bauman K, Liu B, Tuzhilin A (2017) Aspect based recommendations: Recommending items with the most valuable aspects based on user reviews. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining; ACM, Halifax. NS, Canada, pp 717–725. https://doi.org/10.1145/3097983.3098170

  7. Bi X, Qu A, Shen X (2018) Multilayer tensor factorization with applications to recommender systems. The Annals of Statistics 46(6B):3308–3333

    Article  MathSciNet  Google Scholar 

  8. Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics 5:135–146

    Article  Google Scholar 

  9. D’Addio RM, Domingues MA, Manzato MG (2017) Exploiting feature extraction techniques on users’ reviews for movies recommendation. Journal of the Brazilian Computer Society 23(1):7

    Article  Google Scholar 

  10. Frolov E, Oseledets IV (2017) Tensor methods and recommender systems. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(3). https://doi.org/10.1002/widm.1201

  11. Gupta S, Kant V (2020) An aggregation approach to multi-criteria recommender system using genetic programming. Evolving Systems 11(1):29–44. https://doi.org/10.1007/s12530-019-09296-3

    Article  Google Scholar 

  12. Gupta S, Kant V (2020) A review and classification of multi-criteria recommender systems. In: Proceedings of the 4th international conference on intelligent computing and control systems (ICICCS); pp 1156–1162. https://doi.org/10.1109/ICICCS48265.2020.9120983

  13. Hong M, Jung JJ (2018) Multi-sided recommendation based on social tensor factorization. Inf Sci 447:140–156. https://doi.org/10.1016/j.ins.2018.03.019

    Article  Google Scholar 

  14. Hong M (2021) Decrease and conquer-based parallel tensor factorization for diversity and real-time of multi-criteria recommendation. Information Sciences 562:259–278. https://doi.org/10.1016/j.ins.2021.02.005

    Article  MathSciNet  Google Scholar 

  15. Hong M, Jung JJ (2021) Multi-criteria tensor model for tourism recommender systems. Expert Systems with Applications 170:114537. https://doi.org/10.1016/j.eswa.2020.114537

    Article  Google Scholar 

  16. Hu S, Kumar A, Al-turjman F, Gupta S, Seth S (2020) Reviewer credibility and sentiment analysis based user profile modelling for online product recommendation. IEEE Access 8:26172–26189. https://doi.org/10.1109/ACCESS.2020.2971087

    Article  Google Scholar 

  17. Hutto CJ, Gilbert E (2014) VADER: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the eighth international conference on weblogs and social media; Adar E, Resnick P, Choudhury MD, Hogan B, Oh AH Eds.; Ann Arbor, Michigan, USA, June 1-4

  18. Jabreel M, Maaroof N, Valls A, Moreno A (2021) Introducing sentiment analysis of textual reviews in a multi-criteria decision aid system. Appl Sci 11(1):216

    Article  Google Scholar 

  19. Joulin A, Grave E, Bojanowski P, Mikolov T (2017) Bag of tricks for efficient text classification. In: Lapata M, Blunsom P, Koller A (eds) Proceedings of the 15th conference of the European chapter of the association for computational linguistics. Valencia, Spain, pp 427–431. https://doi.org/10.18653/v1/e17-2068

  20. Kant V, Jhalani T, Dwivedi P (2018) Enhanced multi-criteria recommender system based on fuzzy bayesian approach. Multimedia Tools and Applications 77(10):12935–12953. https://doi.org/10.1007/s11042-017-4924-2

    Article  Google Scholar 

  21. Liu H, Li Y, Tsang M, Liu Y (2019) Costco: A neural tensor completion model for sparse tensors. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, KDD, ACM, Anchorage. AK, USA, pp 324–334. https://doi.org/10.1145/3292500.3330881

  22. Ljajic A, Marovac U (2019) Improving sentiment analysis for twitter data by handling negation rules in the serbian language. Computer Science and Information Systems 16(1):289–311. https://doi.org/10.2298/CSIS180122013L

    Article  Google Scholar 

  23. Loria S, Keen P, Honnibal M, Yankovsky R, Karesh D, Dempsey E et al (2014) Textblob: simplified text processing. Secondary TextBlob:, Simplified Text Processing, p 3

  24. Musto C, de Gemmis M, Semeraro G, Lops P (2017) A multi-criteria recommender system exploiting aspect-based sentiment analysis of users’ reviews. In: Cremonesi P, Ricci F, Berkovsky S, Tuzhilin A (eds) Proceedings of the eleventh ACM conference on recommender systems. ACM, Como, Italy, pp 321–325. https://doi.org/10.1145/3109859.3109905

  25. Nassar N, Jafar A, Rahhal Y (2020) A novel deep multi-criteria collaborative filtering model for recommendation system. KnowledgeBased Systems, p 187. https://doi.org/10.1016/j.knosys.2019.06.019

  26. Oh S, Park N, Sael L, Kang U (2018) Scalable tucker factorization for sparse tensors - algorithms and discoveries. In: Proceedings of the 34th IEEE international conference on data engineering, IEEE Computer Society. France, Paris, pp 1120–1131. https://doi.org/10.1109/ICDE.2018.00104

  27. Pappas N, Popescu-Belis A (2016) Adaptive sentiment-aware one-class collaborative filtering. Expert Systems with Applications 43:23–41. https://doi.org/10.1016/j.eswa.2015.08.035

    Article  Google Scholar 

  28. Shambour Q (2021) A deep learning based algorithm for multi- criteria recommender systems. Knowledge-Based Systems 211: 106545. https://doi.org/10.1016/j.knosys.2020.106545

    Article  Google Scholar 

  29. Singh M (2018) Scalability and sparsity issues in recommender datasets: a survey. Knowl Inf Syst, pp 1–43

  30. Song K, Gao W, Feng S, Wang D, Wong K, Zhang C (2017) Recommendation vs sentiment analysis: A text-driven latent factor model for rating prediction with cold-start awareness. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence; Sierra C Ed.; ijcai.org, Melbourne, Australia, pp 2744–2750. https://doi.org/10.24963/ijcai.2017/382

  31. Stratigi M, Li X, Stefanidis K, Zhang Z (2019) Ratings vs. reviews in recommender systems: a case study on the amazon movies dataset. In: Welzer T, Eder J, Podgorelec V, Wrembel R, Ivanovic M, Gamper J, Morzy M, Tzouramanis T, Darmont J, Latific AK (eds) Proceedings of the New Trends in Databases and Information Systems, ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium; Springer, Bled, Slovenia, Communications in Computer and Information Science, 1064, pp 68–76. https://doi.org/10.1007/978-3-030-30278-8_9

  32. Tallapally D, Sreepada RS, Patra BK, Babu KS (2018) User preference learning in multi-criteria recommendations using stacked auto encoders. In: Pera S, Ekstrand MD, Amatriain X, O’Donovan J (eds) Proceedings of the 12th ACM conference on recommender systems; ACM, Vancouver. BC, Canada, pp 475–479. https://doi.org/10.1145/3240323.3240412

  33. Talpada H, Halgamuge MN, Vinh NTQ (2019) An analysis on use of deep learning and lexical-semantic based sentiment analysis method on twitter data to understand the demographic trend of telemedicine. In: Proceedings of the 11th international conference on knowledge and systems engineering; IEEE, Da Nang, Vietnam, pp 1–9. https://doi.org/10.1109/KSE.2019.8919363

  34. Wang X, Ounis I, Macdonald C (2019) Comparison of sentiment analysis and user ratings in venue recommendation. In: Azzopardi L, Stein B, Fuhr N, Mayr P, Hauff C, Hiemstra D (eds) Proceedings of the 41st European conference on advances in information retrieval IR research; Springer, Cologne, Germany, 11437, pp 215–228. https://doi.org/10.1007/978-3-030-15712-8_14

  35. Wasid M, Ali R (2018) An improved recommender system based on multi-criteria clustering approach. Procedia Computer Science 131:93–101

    Article  Google Scholar 

  36. Yargic A, Bilge A (2019) Privacy-preserving multi-criteria collaborative filtering. Information Processing & Management 56(3):994–1009. https://doi.org/10.1016/j.ipm.2019.02.009

    Article  Google Scholar 

  37. Yousif A, Niu Z, Tarus JK, Ahmad A (2019) A survey on sentiment analysis of scientific citations. Artificial Intelligence Review 52(3):1805–1838. https://doi.org/10.1007/s10462-017-9597-8

    Article  Google Scholar 

  38. Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: a survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4). https://doi.org/10.1002/widm.1253

  39. Zheng X, Luo Y, Sun L, Zhang J, Chen F (2018) A tourism destination recommender system using users’ sentiment and temporal dynamics. Journal of Intelligent Information Systems 51(3):557– 578. https://doi.org/10.1007/s10844-018-0496-5

    Article  Google Scholar 

  40. Zheng Y (2017) Criteria chains: a novel multi-criteria recommendation approach. In: Proceedings of the 22nd International conference on intelligent user interfaces. Limassol, Cyprus, pp 29–33. https://doi.org/10.1145/3025171.3025215

  41. Zheng Y (2019) Utility-based multi-criteria recommender systems. In: Hung C, Papadopoulos G A (eds) Proceedings of the 34th ACM/SIGAPP symposium on applied computing. ACM, Limassol, Cyprus, pp 2529–2531. https://doi.org/10.1145/3297280.3297641

  42. Zheng Y, Mobasher B, Burke RD (2015) Carskit: A java-based context-aware recommendation engine. In: Proceedings of the IEEE international conference on data mining workshop; Atlantic. NJ, USA, pp 1668–1671. https://doi.org/10.1109/ICDMW.2015.222

  43. Zhu Y, Shen X, Ye C (2016) Personalized prediction and sparsity pursuit in latent factor models. J Am Stat Assoc 111(513):241–252

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2020R1A2B5B01002207).

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Authors and Affiliations

  1. Smart Tourism Research Center, Kyung-Hee University, 26 Kyungheedae-ro, Seoul, Korea

    Minsung Hong

  2. Department of Computer Engineering, Chung-Ang University, 84 Heukseok-ro, Seoul, Korea

    Jason J. Jung

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Contributions

Conceptualization: Minsung Hong; Methodology: Minsung Hong; Formal analysis and investigation: Minsung Hong; Software: Minsung Hong; Visualization: Minsung Hong; Validation: Minsung Hong; Writing - original draft preparation: Minsung Hong; Writing - review and editing: Minsung Hong and Jason J. Jung; Project administration: Jason J. Jung; Funding acquisition: Jason J. Jung; Resources: Minsung Hong; Supervision: Jason J. Jung; Data curation: Minsung Hong.

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Correspondence to Jason J. Jung.

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Hong, M., Jung, J.J. Sentiment aware tensor model for multi-criteria recommendation. Appl Intell 52, 15006–15025 (2022). https://doi.org/10.1007/s10489-022-03267-z

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