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Contextual Recommender Systems in Business from Models to Experiments

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Machine Learning and Data Analytics for Solving Business Problems

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

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Abstract

Within the last decade, several researchers have focused on using contextual information to design new systems that generate personalized recommendations matching users specific contexts. In this respect, Recommended Systems (RS) have been used in different domains to assist users decision making by providing item recommendations and thereby improving the quality. Context-Aware Recommender Systems (CARS) go further, taking contexts (e.g., time, location, occasion, etc.) into consideration to suggest items that are appropriate to users specific contextual situations. We give in this chapter an overview of the current state of the art in CARS and decision-making process, associated types, challenges, limitations, and business adoptions. Experimental evaluation processes that can be performed to assess the quality of any contextual recommendation system is discussed. Also, an empirical evaluation between CARS and baseline recommender systems is performed on two benchmark datasets.

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Notes

  1. 1.

    https://www.youtube.com.

  2. 2.

    https://www.pandora.com.

  3. 3.

    https://facebook.com/.

  4. 4.

    https://twitter.com.

  5. 5.

    https://www.google.com.

  6. 6.

    https://fr.linkedin.com.

  7. 7.

    http://www.netflixprize.com.

  8. 8.

    http://context-07.ruc.dk.

  9. 9.

    http://www.oxfordlearnersdictionaries.com/.

  10. 10.

    http://wordnetweb.princeton.edu/perl/webwn.

  11. 11.

    http://dictionary.cambridge.org/dictionary/.

  12. 12.

    https://www.merriam-webster.com/dictionary/.

  13. 13.

    https://www.Mckinsey.com/.

  14. 14.

    https://techpp.com/2020/03/09/personalised-restaurant-recommendations-google-maps/.

  15. 15.

    https://www.yelp.com.

References

  1. G. Adomavicius et al., Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23(1), 103–145 (2005)

    Article  Google Scholar 

  2. M. Al-Ghossein, Context-aware recommender systems for real-world applications. Ph.D. Thesis. Université Paris-Saclay (ComUE) (2019)

    Google Scholar 

  3. K. Arour, S. Zammali, A. Bouzeghoub, Test-bed building process for context-aware peer-to-peer information retrieval evaluation. Int. J. Space Based Situated Comput. 5(1), 23–38 (2015). https://doi.org/10.1504/IJSSC.2015.067980

    Article  Google Scholar 

  4. L. Baltrunas, B. Ludwig, F. Ricci, Matrix factorization techniques for context aware recommendation, in Proceedings of the Fifth ACM Conference on Recommender Systems (2011), pp. 301–304

    Google Scholar 

  5. L. Baltrunas, B. Ludwig, F. Ricci, Matrix factorization techniques for context aware recommendation, in Proceedings of the Fifth ACM Conference on Recommender Systems. RecSys ’11. Chicago (2011), pp. 301–304. ISBN:978-1-4503-0683-6

    Google Scholar 

  6. J. Beel, S. Langer, A comparison of offline evaluations, online evaluations, and user studies in the context of research-paper recommender systems, in Research and Advanced Technology for Digital Libraries, ed. by S. Kapidakis, C. Mazurek, M. Werla (Springer International Publishing, Cham, 2015), pp. 153–168

    Chapter  Google Scholar 

  7. R. Bell, Y. Koren, C. Volinsky, Modeling relationships at multiple scales to improve accuracy of large recommender systems, in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2007), pp. 95–104

    Google Scholar 

  8. J. Bennett, S. Lanning, N. Netflix, The Netflix Prize, in In KDD Cup and Workshop in Conjunction with KDD (2007)

    Google Scholar 

  9. E. Blanchard, M. Harzallah, P. Kuntz, A generic framework for comparing semantic similarities on a subsumption hier archy, in Proceedings of the 2008 Conference on ECAI 2008: 18th European Conference on Artificial Intelligence (IOS Press, Amsterdam, 2008), pp. 20–24. ISBN: 978-1-58603-891-5. http://dl.acm.org/citation.cfm?id=1567281.1567291

    Google Scholar 

  10. M. Braunhofer, F. Ricci, Selective contextual information acquisition in travel recommender systems. Inf. Technol. Tour. 17(1), 5–29 (2017)

    Article  Google Scholar 

  11. R. Burke, Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  12. P. Castells, N.J. Hurley, S. Vargas, Novelty and Diversity in Recommender Systems. Recommender Systems Handbook (Springer, Boston, 2015)

    Book  Google Scholar 

  13. K. Chapphannarungsri, S. Maneeroj, Combining multiple criteria and multidimension for movie recommender system, in Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 1 (2009)

    Google Scholar 

  14. G. Chen, D. Kotz, A survey of context-aware mobile computing research. in Dartmouth Computer Science Technical Report TR2000-381 (2000)

    Google Scholar 

  15. A. Civit-Balcells, L. Fernandez-Luque, F. Luna-Perejon, H. de Vries, Analyzing, analyzing recommender systems for health promotion using a multidisciplinary taxonomy: a scoping review. Int. J. Med. Inform. 1 (2017). https://doi.org/10.1016/j.ijmedinf.2017.12.018

  16. A.K. Dey, G.D. Abowd, D. Salber, A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum. Comput. Interact. 16(2–4), 97–166 (2001)

    Article  Google Scholar 

  17. R. Dridi et al., An improved context-aware matrix factorization model incorporating fuzzy measures, in 2018 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2018, Rio de Janeiro, July 8–13, 2018 (IEEE, Piscataway, 2018), pp. 1–8. https://doi.org/10.1109/FUZZ-IEEE.2018.8491439

    Google Scholar 

  18. R. Dridi et al., Effective rating prediction based on selective contextual information. Inf. Sci. 510, 218–242 (2020)

    Article  Google Scholar 

  19. D. Gavalas et al., Mobile recommender systems in tourism. J. Netw. Comput. Appl. 39, 319–333 (2014)

    Article  Google Scholar 

  20. D. Goldberg et al., Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  21. G. Guo et al., LibRec: a Java library for recommender systems, in Posters, Demos, Late-breaking Results and Workshop Proceedings of the 23rd Conference on User Modeling, Adaptation, and Personalization (UMAP 2015), Dublin, June 29–July 3, 2015, ed. by A.I. Cristea et al., vol. 1388. CEUR Workshop Proceedings. CEUR-WS.org (2015). http://ceur-ws.org/Vol-1388/demo_paper1.pdf

  22. L. Hong et al., Context-aware recommendation using role-based trust network. ACM Trans. Knowl. Discov. Data 10(2), 1–25 (2015)

    Article  Google Scholar 

  23. Jhamb, Yogesh, “Machine Learning Models for Context-Aware Recommender Systems” (2018). Engineering Ph.D. Theses. 15. https://scholarcommons.scu.edu/eng_phd_theses/15

  24. M. Jin et al., Combining deep learning and topic modeling for review understanding in context-aware recommendation, in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers) (2018), pp. 1605–1614

    Google Scholar 

  25. Y. Kim, S.B. Cho, A recommendation agent for mobile phone users using bayesian behavior prediction, in Proceedings of the Third International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, Sliema (2009), pp. 283–288

    Google Scholar 

  26. Y. Koren, Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data 4(1), 1:1–1:24 (2010). ISSN: 1556-4681

    Google Scholar 

  27. A. Košir et al., Database for contextual personalization. Elek Trotehniski Vestnik 78(5), 270–274 (2011)

    Google Scholar 

  28. B. Lamche et al., Context-aware recommendations for mobile shopping, in Proceedings of the Workshop on Location-Aware Recommendations, LocalRec, Co-located with the 9th ACM Conference on Recommender Systems (RecSys), Vienna, September 19 (2015), pp. 21–27

    Google Scholar 

  29. Q.-H. Le et al., A state-of-the-art survey on context-aware recommender systems and applications. Int. J. Knowl. Syst. Sci. 12(3), 1–20 (2021)

    Article  Google Scholar 

  30. Q. Li, C. Wang, G. Geng, Improving personalized services in mobile commerce by a novel multicriteria rating approach. in Proceedings of the 17th International Conference on World Wide Web (2008), pp. 1235–1236

    Google Scholar 

  31. A. Livne et al., Deep context-aware recommender system utilizing sequential latent context (2019). Preprint. arXiv:1909.03999

    Google Scholar 

  32. P. Lops, M. De Gemmis, G. Semeraro, Content-based recommender systems: state of the art and trends, in Recommender Systems Handbook (Springer, Berlin, 2011), pp. 73–105

    Book  Google Scholar 

  33. Z. Meng et al., Variational bayesian context-aware representation for grocery recommendation (2019). Preprint. arXiv:1909.07705

    Google Scholar 

  34. F. Meyer, Recommender systems in industrial contexts (2012). Preprint. arXiv:1203.4487

    Google Scholar 

  35. S. Michael, W. Peter, War and Society in Twentieth-Century France, ed. by M. Scriven, P. Wagstaff (Martin’s Press, New York, 1991), xii, 304 p., [4] p. of plates. ISBN: 0854962921

    Google Scholar 

  36. M. Nilashi et al., Analysis of travellers’ online reviews in social networking sites using fuzzy logic approach. Int. J. Fuzzy Syst. 21(5), 1367–1378 (2019)

    Article  Google Scholar 

  37. A. Odić et al., A.: relevant context in a movie recommender system: users’ opinion vs. statistical detection, in Proceedings of the 4th Workshop on Context-Aware Recommender Systems, CARS, September 9, Dublin (2012)

    Google Scholar 

  38. C. Ono et al., Context-Aware Preference Model Based on a Study of Difference Between Real and Supposed Situation Data (Springer, Berlin, 2009), pp. 102–113

    Google Scholar 

  39. A.M. Otebolaku, M.T. Andrade, Context-aware personalization using neighborhood-based context similarity, in Wireless Personal Communications (2016), pp. 1–24

    Google Scholar 

  40. N. Polatidis, C.K. Georgiadis, Mobile recommender systems: an overview of technologies and challenges, in 2013 Second International Conference on Informatics & Applications (ICIA) (IEEE, Piscataway, 2013), pp. 282–287

    Google Scholar 

  41. P. Resnick, H.R. Varian, Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  42. P. Resnick et al., GroupLens: an open architecture for collaborative filtering of netnews, in Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (1994), pp. 175–186

    Google Scholar 

  43. F. Ricci, L. Rokach, B. Shapira, Introduction to recommender systems handbook, in Recommender Systems Handbook (Springer, Berlin, 2011), pp. 1–35

    MATH  Google Scholar 

  44. C. Richthammer, G. Pernul, Situation awareness for recommender systems. Electron. Commer. Res., 1–24 (2018)

    Google Scholar 

  45. K.V. Rodpysh, S.J. Mirabedini, T. Banirostam, Correction to: employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems. Electron. Commer. Res. 22(1), 223 (2022)

    Google Scholar 

  46. I.H. Sarker, A machine learning based robust prediction model for real-life mobile phone data. Internet Things 5, 180–193 (2019)

    Article  Google Scholar 

  47. I.H. Sarker, Y.B. Abushark, A.I. Khan, ContextPCA: predicting context-aware smartphone apps usage based on machine learning techniques. Symmetry 12(4), 499 (2020)

    Google Scholar 

  48. B.N. Schilit, M.M. Theimer, Disseminating active map information to mobile hosts. IEEE Netw. 8(5), 22–32 (1994). ISSN: 0890-8044. https://doi.org/10.1109/65.313011

    Article  Google Scholar 

  49. A. Sen, M. Larson, From sensors to songs: a learning-free novel music recommendation system using contextual sensor data, in Proceedings of the Workshop on Location-Aware Recommendations, Local-Rec, Vienna, September 19 (2015), pp. 40–43

    Google Scholar 

  50. Y. Shi, M.A. Larson, A. Hanjalic, List-wise learning to rank with matrix factorization for collaborative filtering, in Proceedings of the Fourth ACM Conference on Recommender Systems (2010), pp. 269–272. ISBN: 978-1-60558-906-0

    Google Scholar 

  51. K. Shin et al., One4all user representation for recommender systems in E-commerce. CoRR abs/2106.00573 (2021). arXiv: 2106.00573. https://arxiv.org/abs/2106.00573

  52. T. Stepan et al., Incorporating spatial, temporal, and social context in recommendations for location-based social networks. IEEE Trans. Comput. Soc. Syst. 3(4), 164–175 (2016)

    Article  Google Scholar 

  53. H.-M. Wang, G. Yu, Personalized recommendation system K neighbor algorithm optimization, in Proceedings of International Conference on Information Technologies in Education and Learning (ICITEL 2015), Atlantis Press. pp. 1–4

    Google Scholar 

  54. Y. Zheng, Context-aware collaborative filtering using context similarity: an empirical comparison. Information 13(1), 42 (2022)

    Google Scholar 

  55. Y. Zheng, R. Burke, B. Mobasher, Recommendation with differential context weighting, in 21th International Conference, Rome, June 10–14 (2013)

    Google Scholar 

  56. Y. Zheng, B. Mobasher, R. Burke, Incorporating context correlation into context-aware matrix factorization, in Proceedings of the International Conference on Constraints and Preferences for Configuration and Recommendation and Intelligent Techniques for Web Personalization, Buenos Aires (2015), pp. 21–27

    Google Scholar 

  57. Y. Zheng, B. Mobasher, R.D. Burke, CARSKit: a Java-based context-aware recommendation engine, in IEEE International Conference on Data Mining Workshop, ICDMW, Atlantic City, November 14–17 (2015), pp. 1668–1671

    Google Scholar 

  58. X. Zheng et al., A new recommender system using context clustering based on matrix factorization techniques. Chinese J. Electron. 25(2), 334–340 (2016)

    Article  Google Scholar 

  59. C.-N. Ziegler et al., Improving recommendation lists through topic diversification, in Proceedings of the 14th International Conference on World Wide Web, WWW ’05, Chiba (ACM, New York, 2005), pp. 22–32. ISBN: 1-59593-046-9. https://doi.org/10.1145/1060745.1060754. http://doi.acm.org/10.1145/1060745.1060754

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Author information

Authors and Affiliations

  1. College of Business, University of Jeddah, Jeddah, Saudi Arabia

    Khedija Arour

  2. LISI Laboratory, INSAT, University of Carthage, Tunis, Tunisia

    Rim Dridi

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  1. Khedija Arour
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  2. Rim Dridi
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Correspondence to Khedija Arour .

Editor information

Editors and Affiliations

  1. University of Jeddah, Jeddah, Saudi Arabia

    Bader Alyoubi

  2. University of Jeddah, Jeddah, Saudi Arabia

    Chiheb-Eddine Ben Ncir

  3. University of Jeddah, Jeddah, Saudi Arabia

    Ibraheem Alharbi

  4. University of Sfax, Sfax, Tunisia

    Anis Jarboui

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Arour, K., Dridi, R. (2022). Contextual Recommender Systems in Business from Models to Experiments. In: Alyoubi, B., Ben Ncir, CE., Alharbi, I., Jarboui, A. (eds) Machine Learning and Data Analytics for Solving Business Problems. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-031-18483-3_7

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