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ICARE: An Intuitive Context-Aware Recommender with Explanations

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Advances in Smart Healthcare Paradigms and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 244))

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Abstract

The chapter presents a framework, called Intuitive Context-Aware Recommender with Explanations (ICARE), that can provide contextual recommendations, together with their explanations, useful to achieve a specific and predefined goal. We apply ICARE in the healthcare scenario to infer personalized recommendations related to the activities (fitness and rest periods) a specific user should follow or avoid in order to obtain a high value for the sleep quality score, also on the base of their current context and the physical activities performed during the past days. We leverage data mining techniques to extract frequent and context-aware sequential rules that can be used both to provide positive and negative recommendations and to explain them.

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Abbreviations

RS:

Recommendation Systems

HRS:

Health Recommendation System

CARS:

Context-Aware Recommendation Systems

ICARE:

Intuitive Context-Aware Recommender with Explanations

ALBA:

Aged LookBackApriori (ALBA)

References

  1. Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Mag. 32(3), 67–80 (2011). https://doi.org/10.1609/aimag.v32i3.2364

    Article  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of VLDB’94, pp. 487–499. Morgan Kaufmann (1994). http://www.vldb.org/conf/1994/P487.PDF

  3. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Philip, S.Y., Chen A.L.P. (eds.) Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14. IEEE Computer Society, Taipei, Taiwan (1995). https://doi.org/10.1109/ICDE.1995.380415

  4. Ale, J.M., Rossi, G.H.: An approach to discovering temporal association rules. In: Proceedings of the 2000 ACM Symposium on Applied Computing, vol. 1, pp. 294–300 (2000)

    Google Scholar 

  5. Angelides, M.C., Wilson, L.A.C., Echeverría, P.L.B.: Wearable data analysis, visualisation and recommendations on the go using android middleware. Multimed. Tools Appl. 77(20), 26397–26448 (2018)

    Google Scholar 

  6. Balog, K., Radlinski, F., Arakelyan, S.: Transparent, scrutable and explainable user models for personalized recommendation. ACM SIGIR 2019, 265–274 (2019)

    Google Scholar 

  7. Bosoni, P., Meccariello, M., Calcaterra, V., Larizza, C., Sacchi, L., Bellazzi, R.: Deep learning applied to blood glucose prediction from flash glucose monitoring and fitbit data. In: Proceedings of AIME 2020, pp. 59–63. Springer (2020)

    Google Scholar 

  8. Chang, J.H., Lee, W.S.: Finding recently frequent itemsets adaptively over online transactional data streams. Inf. Syst. 31(8), 849–869 (2006). https://doi.org/10.1016/j.is.2005.04.001

  9. Chen, X., Zhang, Y., Wen, J.-R.: Measuring “Why” in recommender systems: a comprehensive survey on the evaluation of explainable recommendation (2022). 2202.06466

    Google Scholar 

  10. Combi, C., Amico, B., Bellazzi, R., Holzinger, A., Moore, J.H., Zitnik, M., Holmes, J.H.: A manifesto on explainability for artificial intelligence in medicine. Artif. Intell. Med. 133, 102423 (2022). ISSN:0933-3657

    Google Scholar 

  11. Cormode, G., Shkapenyuk, V., Srivastava, D., Xu, B.: Forward decay: a practical time decay model for streaming systems. In: 2009 IEEE 25th International Conference on Data Engineering, pp. 138–149 (2009). https://doi.org/10.1109/ICDE.2009.65

  12. De Croon, R., Van Houdt, L., Htun, N.N., Štiglic, G., Vanden Abeele, V., Verbert. K.: Health recommender systems: systematic review. J. Med. Internet Res. 23(6), e18035 (2021)

    Google Scholar 

  13. Doryab, A., Villalba, D.K., Chikersal, P., Dutcher, J.M., Tumminia, M., Liu, X., Cohen, S., Creswell, K., Mankoff, J., Creswell, J.D., et al.: Identifying behavioral phenotypes of loneliness and social isolation with passive sensing: statistical analysis, data mining and machine learning of smartphone and fitbit data. JMIR mHealth uHealth 7(7), e13209 (2019)

    Google Scholar 

  14. Harms, S.K., Deogun, J.S.: Sequential association rule mining with time lags. J. Intell. Inf. Syst. 22(1), 7–22 (2004). https://doi.org/10.1023/A:1025824629047

  15. https://openweathermap.org/api

  16. Marastoni, N., Oliboni, B., Quintarelli, E.: Explainable recommendations for wearable sensor data. In: The 24th International Conference on Big Data Analytics and Knowledge Discovery (DaWak 2022) (2022)

    Google Scholar 

  17. Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)

    Google Scholar 

  18. Rabbi, M., Hane Aung, M., Choudhury, T.: Towards health recommendation systems: an approach for providing automated personalized health feedback from mobile data. In: Rehg, J., Murphy, S., Kumar, S. (eds.) Mobile Health. Springer, Cham (2017)

    Google Scholar 

  19. Ramaswamy, S., Mahajan, S., Silberschatz, A.: On the discovery of interesting patterns in association rules. In VLDB 98, 368–379 (1998)

    Google Scholar 

  20. Salvi, E., Bosoni, P., Tibollo, V., Kruijver, L., Calcaterra, V., Sacchi, L., Bellazzi, R., Larizza, C.: Patient-generated health data integration and advanced analytics for diabetes management: the AID-GM platform. Sensors 20(1), 128 (2020)

    Google Scholar 

  21. Sathyanarayana, A., Joty, S., Fernandez-Luque, L., Ofli, F., Srivastava, J., Elmagarmid, A., Arora, T., Taheri, S.: Sleep quality prediction from wearable data using deep learning. JMIR mHealth uHealth 4(4), e125 (2016)

    Google Scholar 

  22. Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) Advances in database technology—EDBT’96 (Lecture Notes in Computer Science, vol. 1057), pp. 3–17. Springer (1996). https://doi.org/10.1007/BFb0014140

  23. Thambawita, V., Hicks, S.A., Borgli, H., Stensland, H.K., Jha, D., Svensen, M.K., Pettersen, S.A., Johansen, D., Johansen, H.D., Pettersen, S.D., et al.: Pmdata: a sports logging dataset. In: Proceedings of the 11th ACM Multimedia Systems Conference, pp. 231–236 (2020)

    Google Scholar 

  24. Valdez, A.C., Ziefle, M., Verbert, K., Felfernig, A., Holzinger, A.: Recommender systems for health informatics: state-of-the-art and future perspectives. In: Lecture Notes in Computer Science, p. 9605 (2016)

    Google Scholar 

  25. Wang, C., Lizardo, O., Hachen, D.S.: Using fitbit data to examine factors that affect daily activity levels of college students. Plos One 16(1), e0244747 (2021)

    Google Scholar 

  26. Wendt, T., Knaup-Gregori, P., Winter, A.: Decision support in medicine: a survey of problems of user acceptance. In: Medical Infobahn for Europe, pp. 852–856. IOS Press (2000)

    Google Scholar 

  27. Xi, J., Wang, D., Yang, X., Zhang, W., Huang, Q.: Cancer omic data based explainable AI drug recommendation inference: a traceability perspective for explainability. Biomed. Signal Process. Control 79(Part 2), 104144 (2023). ISSN:1746-8094

    Google Scholar 

  28. Yu, P.S., Chi, Y.: Association Rule Mining on Streams, pp. 177–181. Springer New York, New York, NY (2018). https://doi.org/10.1007/978-1-4614-8265-9_25

  29. Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1), 31–60 (2001)

    Google Scholar 

  30. Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., Ma, S.: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Geva, S., Trotman, A., Bruza, P., Clarke, C.L.A., Ja ̈rvelin, K. (eds.) The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’14, Gold Coast , QLD, p. 83–92. ACM (2014)

    Google Scholar 

  31. Zhang, C., Lyu, M., Gan, W., Yu, P.S.: Totally-ordered sequential rules for utility maximization (2022). 2209.13501

    Google Scholar 

  32. Zhou, H., Hirasawa, K.: Evolving temporal association rules in recommender system. Neural Comput. Appl. 31(7), 2605–2619 (2019). https://doi.org/10.1007/s00521-017-3217-z

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Acknowledgements

We thank the editorial team and the reviewers for their expertise and time.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Verona, Strada Le Grazie, 15, 37134, Verona, Italy

    Barbara Oliboni, Anna Dalla Vecchia, Niccolò Marastoni & Elisa Quintarelli

Authors
  1. Barbara Oliboni
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  2. Anna Dalla Vecchia
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  3. Niccolò Marastoni
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  4. Elisa Quintarelli
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Corresponding author

Correspondence to Barbara Oliboni .

Editor information

Editors and Affiliations

  1. Department of Computational Intelligence, Wrocla Universtity of Science and Technology, Wroclaw, Poland

    Halina Kwaśnicka

  2. Inc., The Permanente Medical Group, California, CA, USA

    Nikhil Jain

  3. Department of Computational Intelligence, Wrocla Universtity, Wroclaw, Poland

    Urszula Markowska-Kaczmar

  4. Deakin University, Victoria, VIC, Australia

    Chee Peng Lim

  5. KES International, Selby, UK

    Lakhmi C. Jain

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Oliboni, B., Dalla Vecchia, A., Marastoni, N., Quintarelli, E. (2023). ICARE: An Intuitive Context-Aware Recommender with Explanations. In: Kwaśnicka, H., Jain, N., Markowska-Kaczmar, U., Lim, C.P., Jain, L.C. (eds) Advances in Smart Healthcare Paradigms and Applications. Intelligent Systems Reference Library, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-031-37306-0_4

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