Multimedia Tools and Applications

, Volume 77, Issue 4, pp 4697–4730 | Cite as

Multi-criteria matrix localization and integration for personalized collaborative filtering in IoT environments



Collaborative filtering (CF)-based recommender systems can be used to deal with the complexity problem of users when they want to identify possible tasks on the fly and perform desired tasks by using various smart objects in Internet of Things (IoT) environments. However, in order to use CF-based recommender systems, users need to provide their feedbacks and there are usually more than one criterion considered when users choose an item. Although there have been studies of multi-criteria recommendations, existing approaches require multi-criteria ratings that are explicitly given by users. It is usually a burden for a user to provide more than one instance of feedback on an item; therefore, user feedback datasets are usually sparse when users are asked to provide multi-criteria ratings. Due to the sparsity of multi-criteria rating data, the similarity measurements used by the existing approaches may produce biased results, possibly leading to degradation of the recommendation accuracy. This problem becomes worse as the sparsity of a dataset increases. To alleviate the effects of the data-sparsity problem, and to take advantage of using multi-criteria ratings, we proposed a multi-criteria matrix localization and integration (MCMLI) approach for collaborative filtering in this paper. The main goal of MCMLI is to find cohesive user-item subgroups (CUISs) for each criterion from sparse data, and to predict users’ interests for each criterion in a more precise manner. The proposed approach is composed of three phases. At the first phase, a given user-item matrix is divided into a set of CUIS matrices, each of which is organized with correlated users and items for each criterion. MCMLI repeats this CUIS generation process until the generated subgroups cover all elements of the given user-item matrix. To generate prediction results for each criterion, MCMLI then predicts user ratings on new items for each CUIS and aggregates the prediction results to make recommendations to users. To enable personalized recommendations, during the aggregation process, each user’s preferences on multiple criteria are weighted differently according to the number of CUISs to which the user belongs. We demonstrate the effectiveness of our approach by conducting an experiment with real-world datasets from TripAdvisor and Yahoo! Movies. The experimental results show that MCMLI outperforms existing multi-criteria collaborative-filtering-based recommendation methods in terms of the recommendation accuracy. In addition, unlike the existing multi-criteria recommendation approaches, even when the sparsity level of a dataset increases, the recommendation accuracy of MCMLI does not decrease significantly.


Recommender system Collaborative filtering Multi-criteria recommendation Multi-criteria matrix localization and integration 



This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No.2017-0-00537, Development of Autonomous IoT Collaboration Framework for Space Intelligence).


  1. 1.
    Adomavicius G, Kwon Y-O (2007) New recommendation techniques for multicriteria rating systems. IEEE Intell Syst 22(3):48–55Google Scholar
  2. 2.
    Adomavicius G, Manouselis N, Kwon Y-O (2011) Multi-criteria recommender systems, In: F. Ricci, L. Rokach, B. Shapira and P.B. Kantor (eds.) Recommender Systems Handbook, Springer US, pp. 769–803, 2011.Google Scholar
  3. 3.
    Albani A, Terlouw L, Hardjosumarto G, Dietz LG (2009) Enterprise ontology based service definition, Amsterdam, The Netherlands: 4th International Workshop on Value Modeling and Business OntologiesGoogle Scholar
  4. 4.
    Atzori L, Iera A, Morabito G (2010) The Internet of Things: a survey. Comput Netw 54(15):2787–2805CrossRefMATHGoogle Scholar
  5. 5.
    Banerjee A, I. Dhillon, J. Ghosh, S. Merugu, and D. S. Modha, A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation, Proc. 10th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD ‘04), pp. 509–514, 2004.Google Scholar
  6. 6.
    David HA (1988) The method of paired comparisons. Oxford University Press, New YorkMATHGoogle Scholar
  7. 7.
    Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113CrossRefGoogle Scholar
  8. 8.
    Hsu H, Lachenbruch PA (2008) Paired t test. Wiley Encyclopedia of Clinical Trials. doi: 10.1002/9780471462422.eoct969
  9. 9.
    Jannach D, Karakaya Z, Gedikli F (2012) Accuracy improvements for multi-criteria recommender systems, Proc. 13th ACM conference on Electronic Commerce (EC’12), pp. 674–689Google Scholar
  10. 10.
    Kim B, Kim T, Lee D, Hyun SJ (Feb. 2014) SpinRadar: a spontaneous service provision middleware for place-aware social interactions. Pers Ubiquit Comput 18(2):413–236CrossRefGoogle Scholar
  11. 11.
    Konstan AJ, Miller BN, Maltz D, Herlocker JL, Gordon LR, Riedl J (1997) GroupLens: applying collaborative filtering in recommender systems. Commun ACM 40(3):77–87CrossRefGoogle Scholar
  12. 12.
    Larose DT (2005) k-Nearest neighbor algorithm, discovering knowledge in data: An Introduction to Data MiningGoogle Scholar
  13. 13.
    Lee D, Seung H (2001) Algorithms for non-negative matrix factorization, Adv Neural Inf Proces Syst, 556–562Google Scholar
  14. 14.
    Lee J, Sun M, Lebanon G (2012) PREA: personalized recommendation algorithms toolkit. J, Mach Learn Res 13(1):2699–2703MathSciNetMATHGoogle Scholar
  15. 15.
    Lee J, Kim S, Lebanon G, Singer Y (2013) Local Low-Rank Matrix Approximation, J. Machine Learning Research: Workshop and Conference Proceedings, vol. 28, no. 2, pp. 82–90Google Scholar
  16. 16.
    Liu L, Mehandjiev N, Xu DL (2011) Multi-criteria Service Recommendation based on Users,” Proc. 5th ACM conference on Recommender System (RecSys’11), pp. 77–84Google Scholar
  17. 17.
    Ma H, Yang H, Lyu MR, King I (2008) SoRec: social recommendation using probabilistic matrix factorization, Proc. Seventeenth ACM Conf. Information and Knowledge Management (CIKM’08), pp. 931–940Google Scholar
  18. 18.
    Manouselis N, Costopoulou C (2007) Experimental analysis of design choices in multiattribute utility collaborative filtering. Int J Pattern Recognit Artif Intell 21(2):311–331CrossRefGoogle Scholar
  19. 19.
    Mashal I, Alsaryrah O, Chung T-Y (2016) Performance evaluation of recommendation algorithms on Internet of Things services. Physica A: Statistical Mechanics and its Applications 451:646–656CrossRefGoogle Scholar
  20. 20.
    Mohsen J, Lakshmanan L (2013) HeteroMF: recommendation in heterogeneous information networks using context dependent factor models, Proc. 22nd International Conference on World Wide Web (WWW’13), pp. 643–654Google Scholar
  21. 21.
    Nilashi M, Ibrahim OB, Ithnin N (2014) Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-fuzzy system. Knowl-Based Syst 60:82–101CrossRefGoogle Scholar
  22. 22.
    Nilashi M, Jannach D, Ibrahim OB, Ithnin N (Feb. 2015) Clustering- and regression-based multi-criteria collaborative filtering with incremental updates. Inf Sci 293(1):235–250CrossRefGoogle Scholar
  23. 23.
    Onuma K, Tong H, Faloutos C, (2009) Tangent: a novel, ‘surprise me’, recommendation algorithm, Proc. 15th ACM SIGKDD Int. Conf. Knowledge Discovery and Data mining (KDD’09), pp. 657–666Google Scholar
  24. 24.
    Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. Statistics 2–5:2007Google Scholar
  25. 25.
    Pilaszy I, Tikk D (2009) Recommending New Movies: Even a Few Ratings Are More Valuable than Metadata, Proc. 3rd ACM conference on Recommender Systems (RecSys’09), pp. 93–100Google Scholar
  26. 26.
    Plageras A, Psannis KE, Ishibashi Y, Kim B-G (2016) IoT-based surveillance system for ubiquitous healthcare, 42nd Annual Conference of IEEE Industrial Electronics Society, Piazza Adua, 1 - Firenze (Florence), ItalyGoogle Scholar
  27. 27.
    Saaty TL (1999) Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS Publications, PittsburghGoogle Scholar
  28. 28.
    Sahoo N, Krishnan R, Duncan G, Callan JP (2006) Collaborative filtering with multi-component rating for recommender systems, Proc. Sixteenth Workshop on Information Technologies and SystemsGoogle Scholar
  29. 29.
    Salakhutdinov R, Mnih A (2008) Probabilistic matrix factorization. Adv Neural Inf Proces Syst:1257–1264Google Scholar
  30. 30.
    Salakhutdinov R, Mnih A, (2008)Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo, Proc. International Conference on Machine Learning (ICML ‘08), pp. 880–887Google Scholar
  31. 31.
    Shambour Q, Lu J (2012) A trust-semantic fusion-based recommendation approach for e-business applications. Decis Support Syst 54(1):768–780CrossRefGoogle Scholar
  32. 32.
    Stergiou C, Psannis KE, Kim B-G, Gupta B, (2016) Secure integration of IoT and cloud computing, Elsevier, Future Generation Computer SystemsGoogle Scholar
  33. 33.
    Su X, Khoshgoftaar T (2009) A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009. doi: 10.1155/2009/421425
  34. 34.
    TripAdvisor Data Set (2017) (Accessed 2017.02.03)
  35. 35.
    Vo CC, Loke SW, Torabi T, Nguyen T (2011) TASKREC: a task-based user interface for smart spaces, Proc. ACM 9th International Conference on Advances in Mobile Computing and Multimedia, pp. 223–226Google Scholar
  36. 36.
    Vo CC, Torabi T, Loke SW (2012) Task-oriented Systems for Interaction with Ubiquitous computing environments, Mobile and Ubiquitous Systems: Computing, Networking, and Services, Springer, Berlin Heidelberg pp. 332–339Google Scholar
  37. 37.
    Weiser M (1991) The computer for the 21st century. Sci Am 265(3):94–104CrossRefGoogle Scholar
  38. 38.
    Xu B, Bu J, Chen C, Cai D (2012) An exploration of improving collaborative recommender systems via user-item subgroups, Proc. Twentyfirst International Conference on World Wide Web (WWW’12), pp. 21–30Google Scholar
  39. 39.
    Zhang X, Cheng J, Yuan T, Niu B, Lu H (2013) TopRec: domain-specific recommendation through community topic mining in social network, Proc. 22nd International Conference on World Wide Web (WWW’13), pp. 1501–1510Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of ComputingKorea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea

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