Multimedia Tools and Applications

, Volume 75, Issue 7, pp 3813–3842 | Cite as

Recommending multimedia visiting paths in cultural heritage applications

  • Ilaria BartoliniEmail author
  • Vincenzo Moscato
  • Ruggero G. Pensa
  • Antonio Penta
  • Antonio Picariello
  • Carlo Sansone
  • Maria Luisa Sapino


The valorization and promotion of worldwide Cultural Heritage by the adoption of Information and Communication Technologies represent nowadays some of the most important research issues with a large variety of potential applications. This challenge is particularly perceived in the Italian scenario, where the artistic patrimony is one of the most diverse and rich of the world, able to attract millions of visitors every year to monuments, archaeological sites and museums. In this paper, we present a general recommendation framework able to uniformly manage heterogeneous multimedia data coming from several web repositories and to provide context-aware recommendation techniques supporting intelligent multimedia services for the users—i.e. dynamic visiting paths for a given environment. Specific applications of our system within the cultural heritage domain are proposed by means of real case studies in the mobile environment related both to an outdoor and indoor scenario, together with some results on user’s satisfaction and system accuracy.


Cultural heritage Multimedia databases Recommender systems Context awareness 



The realization of the proposed prototype was supported by DATABENC,6 a high technology district for Cultural Heritage management recently funded by Regione Campania - Italy.


  1. 1.
    Aart C, Wielinga B, Hage WR (2010) Mobile cultural heritage guide: location-aware semantic search. In: Knowledge engineering and management by the masses, volume 6317 of Lecture notes in computer science, pages 257–271. Springer Berlin HeidelbergGoogle Scholar
  2. 2.
    Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. In: IEEE Transactions on Knowledge and Data Engineering, 17(6):734–749, IEEE Computer SocietyGoogle Scholar
  3. 3.
    Adomavicius G, Zhang J (2010) On the stability of recommendation algorithms. In: ACM Conference on Recommender Systems, pages 47–54, ACMGoogle Scholar
  4. 4.
    Albanese M, Chianese A, d’Acierno A, Moscato V, Picariello A (2010) A multimedia recommender integrating object features and user behavior. In: Multimedia tools and applications, 50(3):563–585, SpringerGoogle Scholar
  5. 5.
    Albanese M, d’Acierno A, Moscato V, Persia F, Picariello A (2010) Modeling recommendation as a social choice problem. In: ACM Conference on Recommender Systems, pages 329–332. ACMGoogle Scholar
  6. 6.
    Albanese M, d’Acierno A, Moscato V, Persia F, Picariello A (2011) A multimedia semantic recommender system for cultural heritage applications. In: IEEE International Conference on Semantic Computing, pages 403–410. IEEE Computer SocietyGoogle Scholar
  7. 7.
    Albanese M, d’Acierno A, Moscato V, Persia F, Picariello A (2013) A multmimedia recommender system. In: ACM Transactions on Internet Technology, 13(1), ACMGoogle Scholar
  8. 8.
    Amato F, Chianese A, Mazzeo A, Moscato V, Picariello A, Piccialli F (2013) The talking museum project. In: International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN-2013Google Scholar
  9. 9.
    Anand SS, Kearney P, Shapcott M (2007) Generating semantically enriched user profiles for web personalization. In: ACM Transactions on Internet Technology, 7(4), ACMGoogle Scholar
  10. 10.
    Ardissono L, Kuflik T, Petrelli D (2012) Personalization in cultural heritage: the road travelled and the one ahead. In: User modeling and user-adapted interaction, 22(1–2):73–99, SpringerGoogle Scholar
  11. 11.
    Bartolini I, Ciaccia P (2008) Imagination: exploiting link analysis for accurate image annotation. In: Adaptive multimedia retrieval: retrieval, user, and semantics, volume 4918/2008 of Lecture notes in computer science, pages 32–44, SpringerGoogle Scholar
  12. 12.
    Bartolini I, Ciaccia P, Patella M (2010) Query processing issues in region-based image databases. In: Knowledge information system, 25(2):389–420, SpringerGoogle Scholar
  13. 13.
    Bartolini I, Moscato V, Pensa RG, Penta A, Picariello A, Sansone C, Sapino ML (2013) Recommending multimedia objects in cultural heritage applications. In: International Conference on Image Analysis and Processing, Workshops, pages 257–267Google Scholar
  14. 14.
    Bartolini I, Patella M, Romani C (2013) Shiatsu: tagging and retrieving videos without worries. In: Multimedia tools and applications, 63(2):357–385, SpringerGoogle Scholar
  15. 15.
    Bartolini I, Zhang Z, Papadias D (2011) Collaborative filtering with personalized skylines. IEEE Trans Knowl Data Eng 23(2):190–203CrossRefGoogle Scholar
  16. 16.
    Basilico J, Hofmann T (2004) Unifying collaborative and content-based filtering. In: International Conference on Machine Learning, pages 65–72, ACMGoogle Scholar
  17. 17.
    Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). In: Computer vision image understanding, 110(3):346–359, ElsevierGoogle Scholar
  18. 18.
    Bellotti F, Berta R, De Gloria A, D’ursi A, Fiore V (2013) A serious game model for cultural heritage. J Comput Cult Herit 5(4):17:1–17:27, ACMGoogle Scholar
  19. 19.
    Bhatt CA, Kankanhalli MS (2011) Multimedia data mining: state of the art and challenges. In: Multimedia tools applications, 51(1):35–76, SpringerGoogle Scholar
  20. 20.
    Bowe JP, Fantonio SF (2004) Personalization and the web from a museum perspective. In: International Conference on Museums and the WebGoogle Scholar
  21. 21.
    Ciaccia P, Patella M, Zezula P (1997) M-tree: an efficient access method for similarity search in metric spaces. In: International Conference on Very Large Data Bases, pages 426–435, Morgan Kaufmann Publishers IncGoogle Scholar
  22. 22.
    Costantini S, Mostarda L, Tocchio A, Tsintza P (2008) Dalica: agent-based ambient intelligence for cultural-heritage scenarios. In: Intelligent systems, 23(2):34–41, IEEEGoogle Scholar
  23. 23.
    Dourish P (2004) What we talk about when we talk about context. In: Personal ubiquitous computer, 8(1):19–30, SpringerGoogle Scholar
  24. 24.
    Galleguillos C, Belongie S (2010) Context based object categorization: a critical survey. In: Computer vision and image understanding, 114(6):712–722, Elsevier. Special Issue on Multi-Camera and Multi-Modal Sensor FusionGoogle Scholar
  25. 25.
    Goodman LA, Kruskal WH (1972) Measures of association for cross classifications, IV: simplification of asymptotic variances. J Am Stat Assoc 67(338):415–421CrossRefzbMATHGoogle Scholar
  26. 26.
    Hart S, Staveland LE (1988) Development of NASA-TLX (task load index): results of empirical and theoretical research. In: Human mental workload pages 139–183Google Scholar
  27. 27.
    Hijikata Y, Iwahama K, Nishida S (2006) Content-based music filtering system with editable user profile. In: ACM Symposium on Applied Computing, pages 1050–1057, ACMGoogle Scholar
  28. 28.
    Ienco D, Robardet C, Pensa RG, Meo R (2013) Parameter-less co-clustering for star-structured heterogeneous data. In Data mining knowledge discovering, 26(2):217–254, SpringerGoogle Scholar
  29. 29.
    Ilyas IF, Beskales G, Soliman MA (2008) A survey of top-k query processing techniques in relational database systems. In: ACM computing surveys, 40(4):11:1–11:58, ACMGoogle Scholar
  30. 30.
    Juszczyszyn K, Kazienko P, Musia K (2010) Personalized ontology-based recommender systems for multimedia objects. In: Agent and multi-agent technology for internet and enterprise systems, studies in computational intelligence, pages 275–292, SpringerGoogle Scholar
  31. 31.
    Kabassi K (2013) Personalisation systems for cultural tourism. In: Multimedia services in intelligent environments, volume 25 of Smart innovation, systems and technologies, pages 101–111, SpringerGoogle Scholar
  32. 32.
    Karaman S, Bagdanov A, D’Amico G, Landucci L, Ferracani A, Pezzatini D, Bimbo A (2013) Passive profiling and natural interaction metaphors for personalized multimedia museum experiences. In: New trends in image analysis and processing, volume 8158 of Lecture notes in computer science, pages 247–256. SpringerGoogle Scholar
  33. 33.
    Karatzoglou A, Amatriain X, Baltrunas L, Oliver N (2010) Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. In: ACM Conference on Recommender Systems pages 79–86, ACMGoogle Scholar
  34. 34.
    Kim JK, Kim HK, Cho YH (2008) A user-oriented contents recommendation system in peer-to-peer architecture. Expert Syst Appl 34(1):300–312, ElsevierCrossRefGoogle Scholar
  35. 35.
    Kim HK, Kim JK, Ryu YU (2009) Personalized recommendation over a customer network for ubiquitous shopping. IEEE Trans Serv Comput 2(2):140–151MathSciNetCrossRefGoogle Scholar
  36. 36.
    Kuflik T, Stock O, Zancanaro M, Gorfinkel A, Jbara S, Kats S, Sheidin J, Kashtan N (2011) A visitor’s guide in an active museum: presentations, communications and reflection. In: Journal computing and cultural heritage, 3(3):11:1–11:25, ACMGoogle Scholar
  37. 37.
    Kuhn HW (1955) The Hungarian method for the assignment problem. In: Naval research logistics quarterly, 2:83–97Google Scholar
  38. 38.
    Lowe D (1999) Object recognition from local scale-invariant features. In: IEEE International Conference Computer Vision, vol. 2, pages 1150–1157Google Scholar
  39. 39.
    Maidel V, Shoval P, Shapira B, Taieb-Maimon M (2008) Evaluation of an ontology-content based filtering method for a personalized newspaper. In: ACM Conference on Recommender Systems, pages 91–98. ACMGoogle Scholar
  40. 40.
    Manzato MG, Goularte R (2009) Supporting multimedia recommender systems with peer-level annotations. In: XV Brazilian Symposium on Multimedia and the Web, pages 26:1–26:8. ACMGoogle Scholar
  41. 41.
    Musial K, Kazienko P, Kajdanowicz T (2008) Social recommendations within the multimedia sharing systems. In: 1st World Summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society, pages 364–372. SpringerGoogle Scholar
  42. 42.
    Pazzani MJ, Billsus D (2007) The adaptive web. In: Content-based recommendation systems, pages 325–341, SpringerGoogle Scholar
  43. 43.
    Ricci F, Rokach L, Shapira B (2011) Recommender systems handbook. Springer, New YorkCrossRefzbMATHGoogle Scholar
  44. 44.
    Salton G (1989) Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley Longman Publishing Co., Inc., BostonGoogle Scholar
  45. 45.
    Schafer JB, Frankowski D, Herlocker J, Sen S (2007) The adaptive web. In: Collaborative filtering recommender systems, pages 291–324, SpringerGoogle Scholar
  46. 46.
    Schulz AG, Hahsler M (2002) Evaluation of recommender algorithms for an internet information broker based on simple association rules and on the repeat-buying theory. In: International Workshop on Mining Web Data for Discovering Usage Patterns and Profiles, volume 2703 of Lecture Notes in Artificial Intelligence, pages 100–114, SpringerGoogle Scholar
  47. 47.
    Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:4:2–4:2, HindawiCrossRefGoogle Scholar
  48. 48.
    Su JH, Yeh HH, Yu PS, Tseng VS (2010) Music recommendation using content and context information mining. In: IEEE Intelligent Systems, 25(1):16–26, IEEEGoogle Scholar
  49. 49.
    Tseng VS, Su JH, Wang BW, Hsiao CY, Huang J, Yeh HH (2008) Intelligent multimedia recommender by integrating annotation and association mining. In: IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, pages 492–499, IEEEGoogle Scholar
  50. 50.
    Vlahakis V, Karigiannis J, Tsotros M, Gounaris M, Almeida L, Stricker D, Gleue T, Christou IT, Carlucci R, Ioannidis N (2001) Archeoguide: first results of an augmented reality, mobile computing system in cultural heritage sites. In: Conference on Virtual Reality Archeology, and Cultural Heritage, pages 131–140. ACMGoogle Scholar
  51. 51.
    Vlahakis V, Pliakas T, Demiris A, Ioannidis N (2003) Design and application of an augmented reality system for continuous, context-sensitive guided tours of indoor and outdoor cultural sites and museums. In: International Conference on Virtual Reality, Archaeology and Intelligent Cultural Heritage, pages 155–164. Eurographics AssociationGoogle Scholar
  52. 52.
    Wang Y, Stash N, Sambeek R, Schuurmans Y, Aroyo L, Schreiber G, Gorgels P (2009) Cultivating personalized museum tours online and on-site. In: Interdisciplinary science reviews, 34(2–3):139–153Google Scholar
  53. 53.
    Weinland D, Ronfard R, Boyer E (2011) A survey of vision-based methods for action representation, segmentation and recognition. In: Computer vision and image understanding, 115(2):224–241, ElsevierGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ilaria Bartolini
    • 1
    Email author
  • Vincenzo Moscato
    • 2
  • Ruggero G. Pensa
    • 3
  • Antonio Penta
    • 3
  • Antonio Picariello
    • 2
  • Carlo Sansone
    • 2
  • Maria Luisa Sapino
    • 3
  1. 1.Department of Computer Science and EngineeringUniversity of BolognaBolognaItaly
  2. 2.Department of Electrical Engineering and Information TechnologyUniversity of Naples Federico IINaplesItaly
  3. 3.Department of Computer ScienceUniversity of TorinoTorinoItaly

Personalised recommendations