Skip to main content
SpringerLink
Log in

Context-Aware Recommender Systems

  • Chapter
Recommender Systems Handbook

Abstract

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, many existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). There is growing understanding that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. We also discuss three popular algorithmic paradigms—contextual pre-filtering, post-filtering, and modeling—for incorporating contextual information into the recommendation process, and survey recent work on context-aware recommender systems. We also discuss important directions for future research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    See, for example, http://www.polytech.univ-savoie.fr/index.php?id=context-13-home, http://context-11.teco.edu, or http://context-07.ruc.dk, for recent instances.

  2. 2.

    For the sake of completeness, we would like to point out that not only the contextual dimensions, but also the traditional User and Item dimensions can have their attributes form hierarchical relationships. For example, the main two dimensions from Example 6.1 can have the following hierarchies associated with them: Movie: MovieID → Genre; User: UserID → Age, UserID → Gender, UserID → Profession.

  3. 3.

    For simplicity and illustration purposes, this figure uses only two-way splits. Obviously, three-way, four-way and, more generally, multi-way splits are also allowed.

  4. 4.

    OLAP stands for OnLine Analytical Processing, which represents a popular approach to manipulation and analysis of data stored in multi-dimensional cube structures and which is widely used for decision support.

References

  1. AWS re:Invent 2012, Day 1 Keynote. http://www.youtube.com/watch?v=8FJ5DBLSFe4. YouTube video; Accessed: 2014-06-28

  2. Abbar, S., Bouzeghoub, M., Lopez, S.: Context-aware recommender systems: A service-oriented approach. VLDB PersDB Workshop (2009)

    Google Scholar 

  3. Abowd, G.D., Atkeson, C.G., Hong, J., Long, S., Kooper, R., Pinkerton, M.: Cyberguide: A mobile context-aware tour guide. Wireless Networks 3(5), 421–433 (1997)

    Article  Google Scholar 

  4. Adomavicius, G., Jannach, D.: Preface to the special issue on context-aware recommender systems. User Model. User-Adapt. Interact. 24(1–2), 1–5 (2014)

    Article  Google Scholar 

  5. Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32(3), 67–80 (2011)

    Google Scholar 

  6. Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS) 23(1), 103–145 (2005)

    Article  Google Scholar 

  7. Adomavicius, G., Tuzhilin, A.: Multidimensional recommender systems: A data warehousing approach. In: L. Fiege, G. Mhl, U. Wilhelm (eds.) Electronic Commerce, Lecture Notes in Computer Science, vol. 2232, pp. 180–192. Springer Berlin Heidelberg (2001). DOI 10.1007/3-540-45598-1_17. URL http://dx.doi.org/10.1007/3-540-45598-1_17

  8. Adomavicius, G., Tuzhilin, A.: Incorporating context into recommender systems using multidimensional rating estimation methods. In: Proceedings of the 1st International Workshop on Web Personalization, Recommender Systems and Intelligent User Interfaces (WPRSIUI 2005) (2005)

    Google Scholar 

  9. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)

    Article  Google Scholar 

  10. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci et al. [79], pp. 217–253

    Google Scholar 

  11. Adomavicius, G., Tuzhilin, A., Zheng, R.: REQUEST: A query language for customizing recommendations. Information System Research 23(1), 99–117 (2011)

    Article  Google Scholar 

  12. Ahn, H., Kim, K., Han, I.: Mobile advertisement recommender system using collaborative filtering: MAR-CF. In: Proceedings of the 2006 Conference of the Korea Society of Management Information Systems,

    Google Scholar 

  13. Alpaydin, E.: Introduction to machine learning. The MIT Press (2004)

    Google Scholar 

  14. Anand, S.S., Mobasher, B.: Contextual recommendation. WebMine, LNAI 4737, 142–160 (2007)

    Google Scholar 

  15. Ansari, A., Essegaier, S., Kohli, R.: Internet recommendation systems. Journal of Marketing Research 37(3), 363–375 (2000)

    Article  Google Scholar 

  16. Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Applied Artificial Intelligence 17(8), 687–714 (2003)

    Article  Google Scholar 

  17. Baltrunas, L., Amatriain, X.: Towards time-dependant recommendation based on implicit feedback. In: Workshop on Context-Aware Recommender Systems (CARS 2009). New York (2009)

    Google Scholar 

  18. Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., Lke, K.H., Schwaiger, R.: Incarmusic: Context-aware music recommendations in a car. In: C. Huemer, T. Setzer (eds.) E-Commerce and Web Technologies, Lecture Notes in Business Information Processing, vol. 85, pp. 89–100. Springer Berlin Heidelberg (2011). DOI 10.1007/978-3-642-23014-1_8. URL http://dx.doi.org/10.1007/978-3-642-23014-1_8

  19. Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context-aware places of interest recommendations for mobile users. In: A. Marcus (ed.) Design, User Experience, and Usability. Theory, Methods, Tools and Practice, Lecture Notes in Computer Science, vol. 6769, pp. 531–540. Springer Berlin Heidelberg (2011). DOI 10.1007/978-3-642-21675-6_61. URL http://dx.doi.org/10.1007/978-3-642-21675-6_61

  20. Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing 16(5), 507–526 (2012)

    Article  Google Scholar 

  21. Baltrunas, L., Ludwig, B., Ricci, F.: Matrix factorization techniques for context aware recommendation. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ’11, pp. 301–304. ACM, New York, NY, USA (2011). DOI 10.1145/2043932.2043988. URL http://doi.acm.org/10.1145/2043932.2043988

  22. Baltrunas, L., Ricci, F.: Context-dependent items generation in collaborative filtering. In: Workshop on Context-Aware Recommender Systems (CARS 2009). New York (2009)

    Google Scholar 

  23. Baltrunas, L., Ricci, F.: Experimental evaluation of context-dependent collaborative filtering using item splitting. User Modeling and User-Adapted Interaction 24(1–2), 7–34 (2014). DOI 10.1007/s11257-012-9137-9. URL http://dx.doi.org/10.1007/s11257-012-9137-9

  24. Bauman, K., Tuzhilin, A.: Discovering contextual information from user reviews for recommendation purposes. In: Proceedings of the ACM RecSys Workshop on New Trends in Content Based Recommender Systems (2014)

    Google Scholar 

  25. Bazire, M., Brézillon, P.: Understanding context before using it. In: A. Dey, et al. (eds.) Proceedings of the 5th International Conference on Modeling and Using Context. Springer-Verlag (2005)

    Google Scholar 

  26. Braunhofer, M., Elahi, M., Ge, M., Ricci, F.: Context dependent preference acquisition with personality-based active learning in mobile recommender systems. In: P. Zaphiris, A. Ioannou (eds.) Learning and Collaboration Technologies. Technology-Rich Environments for Learning and Collaboration - First International Conference, LCT 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22–27, 2014, Proceedings, Part II, Lecture Notes in Computer Science, vol. 8524, pp. 105–116. Springer (2014). DOI 10.1007/978-3-319-07485-6_11. URL http://dx.doi.org/10.1007/978-3-319-07485-6_11

  27. Braunhofer, M., Kaminskas, M., Ricci, F.: Recommending music for places of interest in a mobile travel guide. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ’11, pp. 253–256. ACM, New York, NY, USA (2011). DOI 10.1145/2043932.2043977. URL http://doi.acm.org/10.1145/2043932.2043977

  28. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, vol. 461, pp. 43–52. San Francisco, CA (1998)

    Google Scholar 

  29. Bulander, R., Decker, M., Schiefer, G., Kolmel, B.: Comparison of different approaches for mobile advertising. In: Proceedings of the Second IEEE International Workshop on Mobile Commerce and Services, WMCS ’05, pp. 174–182. IEEE Computer Society, Washington, DC, USA (2005). DOI 10.1109/WMCS.2005.8. URL http://dx.doi.org/10.1109/WMCS.2005.8

  30. Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  31. Burke, R.: Hybrid web recommender systems. The Adaptive Web pp. 377–408 (2007)

    Google Scholar 

  32. Cai, R., Zhang, C., Wang, C., Zhang, L., Ma, W.Y.: Musicsense: Contextual music recommendation using emotional allocation modeling. In: Proceedings of the 15th International Conference on Multimedia, MULTIMEDIA ’07, pp. 553–556. ACM, New York, NY, USA (2007). DOI 10.1145/1291233.1291369. URL http://doi.acm.org/10.1145/1291233.1291369

  33. Campos, P.G., Díez, F., Cantador, I.: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model. User-Adapt. Interact. 24(1–2), 67–119 (2014)

    Article  Google Scholar 

  34. Cantador, I., Castells, P.: Semantic contextualisation in a news recommender system. In: Workshop on Context-Aware Recommender Systems (CARS 2009). New York (2009)

    Google Scholar 

  35. Cena, F., Console, L., Gena, C., Goy, A., Levi, G., Modeo, S., Torre, I.: Integrating heterogeneous adaptation techniques to build a flexible and usable mobile tourist guide. AI Communications 19(4), 369–384 (2006)

    MathSciNet  MATH  Google Scholar 

  36. Chatterjee, S., Hadi, A.S., Price, B.: Regression analysis by example. John Wiley and Sons (2000)

    Google Scholar 

  37. Chaudhuri, S., Dayal, U.: An overview of data warehousing and olap technology. ACM Sigmod record 26(1), 65–74 (1997)

    Article  Google Scholar 

  38. Cheverst, K., Davies, N., Mitchell, K., Friday, A., Efstratiou, C.: Developing a context-aware electronic tourist guide: some issues and experiences. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 17–24. ACM (2000)

    Google Scholar 

  39. Church, K., Smyth, B., Cotter, P., Bradley, K.: Mobile information access: A study of emerging search behavior on the mobile internet. ACM Trans. Web 1(1) (2007). DOI 10.1145/1232722.1232726. URL http://doi.acm.org/10.1145/1232722.1232726

  40. Codina, V., Ricci, F., Ceccaroni, L.: Exploiting the semantic similarity of contextual situations for pre-filtering recommendation. In: S. Carberry, S. Weibelzahl, A. Micarelli, G. Semeraro (eds.) User Modeling, Adaptation, and Personalization, Lecture Notes in Computer Science, vol. 7899, pp. 165–177. Springer Berlin Heidelberg (2013). DOI 10.1007/978-3-642-38844-6_14. URL http://dx.doi.org/10.1007/978-3-642-38844-6_14

  41. De Carolis, B., Mazzotta, I., Novielli, N., Silvestri, V.: Using common sense in providing personalized recommendations in the tourism domain. In: Workshop on Context-Aware Recommender Systems (CARS 2009). New York (2009)

    Google Scholar 

  42. Dourish, P.: What we talk about when we talk about context. Personal and ubiquitous computing 8(1), 19–30 (2004)

    Article  Google Scholar 

  43. Fling, B.: Mobile Design and Development: Practical Concepts and Techniques for Creating Mobile Sites and Web Apps, 1st edn. O’Reilly Media, Inc. (2009)

    Google Scholar 

  44. Gorgoglione, M., Panniello, U., Tuzhilin, A.: The effect of context-aware recommendations on customer purchasing behavior and trust. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ’11, pp. 85–92. ACM, New York, NY, USA (2011). DOI 10.1145/2043932.2043951. URL http://doi.acm.org/10.1145/2043932.2043951

  45. Hariri, N., Mobasher, B., Burke, R.: Context-aware music recommendation based on latent topic sequential patterns. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys ’12, pp. 131–138. ACM, New York, NY, USA (2012). DOI 10.1145/2365952.2365979. URL http://doi.acm.org/10.1145/2365952.2365979

  46. Hariri, N., Mobasher, B., Burke, R.: Query-driven context aware recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys ’13, pp. 9–16. ACM, New York, NY, USA (2013). DOI 10.1145/2507157.2507187. URL http://doi.acm.org/10.1145/2507157.2507187

  47. Hussein, T., Linder, T., Gaulke, W., Ziegler, J.: Context-aware recommendations on rails. In: Workshop on Context-Aware Recommender Systems (CARS 2009). New York, NY, USA (2009)

    Google Scholar 

  48. Hussein, T., Linder, T., Gaulke, W., Ziegler, J.: Hybreed: A software framework for developing context-aware hybrid recommender systems. User Model. User-Adapt. Interact. 24(1–2), 121–174 (2014)

    Article  Google Scholar 

  49. Jannach, D., Hegelich, K.: A case study on the effectiveness of recommendations in the mobile internet. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys ’09, pp. 205–208. ACM, New York, NY, USA (2009). DOI 10.1145/1639714.1639749. URL http://doi.acm.org/10.1145/1639714.1639749

  50. Jiang, T., Tuzhilin, A.: Improving personalization solutions through optimal segmentation of customer bases. IEEE Transactions on Knowledge and Data Engineering 21(3), 305–320 (2009)

    Article  Google Scholar 

  51. Kaminskas, M., Ricci, F.: Contextual music information retrieval and recommendation: State of the art and challenges. Computer Science Review 6(2-3), 89–119 (2012)

    Article  Google Scholar 

  52. Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys ’10, pp. 79–86. ACM, New York, NY, USA (2010). DOI 10.1145/1864708.1864727. URL http://doi.acm.org/10.1145/1864708.1864727

  53. Karatzoglou, A., Baltrunas, L., Church, K., Böhmer, M.: Climbing the app wall: Enabling mobile app discovery through context-aware recommendations. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM ’12, pp. 2527–2530. ACM, New York, NY, USA (2012). DOI 10.1145/2396761.2398683. URL http://doi.acm.org/10.1145/2396761.2398683

  54. Kenteris, M., Gavalas, D., Economou, D.: Electronic mobile guides: a survey. Personal and Ubiquitous Computing 15(1), 97–111 (2011)

    Article  Google Scholar 

  55. Kimball, R., Ross, M.: The data warehousing toolkit. John Wiley & Sons, New York (1996)

    Google Scholar 

  56. Kiseleva, J., Thanh Lam, H., Pechenizkiy, M., Calders, T.: Discovering temporal hidden contexts in web sessions for user trail prediction. In: Proceedings of the 22nd International Conference on World Wide Web Companion, WWW ’13 Companion, pp. 1067–1074. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2013). URL http://dl.acm.org/citation.cfm?id=2487788.2488120

  57. Koller, D., Sahami, M.: Toward optimal feature selection. In: Proceedings of the 13th International Conference on Machine Learning, pp. 284–292. Morgan Kaufmann (1996)

    Google Scholar 

  58. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426–434. ACM, New York, NY (2008)

    Google Scholar 

  59. Koutrika, G., Bercovitz, B., Garcia-Molina, H.: Flexrecs: expressing and combining flexible recommendations. In: Proceedings of the 35th SIGMOD international conference on Management of data, pp. 745–758. ACM, Providence, RI (2009)

    Google Scholar 

  60. Lee, H., Park, S.J.: Moners: A news recommender for the mobile web. Expert Systems with Applications 32(1), 143–150 (2007). DOI http://dx.doi.org/10.1016/j.eswa.2005.11.010. URL http://www.sciencedirect.com/science/article/pii/S0957417405003167

  61. Levandoski, J.J., Ekstrand, M.D., Ludwig, M., Eldawy, A., Mokbel, M.F., Riedl, J.: Recbench: Benchmarks for evaluating performance of recommender system architectures. PVLDB 4(11), 911–920 (2011)

    Google Scholar 

  62. Levandoski, J.J., Eldawy, A., Mokbel, M.F., Khalefa, M.E.: Flexible and extensible preference evaluation in database systems. ACM Trans. Database Syst. 38(3), 17 (2013)

    Article  MathSciNet  Google Scholar 

  63. Liu, H., Motoda, H.: Feature selection for knowledge discovery and data mining. Springer (1998)

    Google Scholar 

  64. Lombardi, S., Anand, S.S., Gorgoglione, M.: Context and customer behavior in recommendation. In: Workshop on Context-Aware Recommender Systems (CARS 2009). New York

    Google Scholar 

  65. Mahmood, T., Ricci, F., Venturini, A.: Improving recommendation effectiveness: Adapting a dialogue strategy in online travel planning. J. of IT & Tourism 11(4), 285–302 (2009). DOI 10.3727/109830510X12670455864203. URL http://dx.doi.org/10.3727/109830510X12670455864203

  66. Moling, O., Baltrunas, L., Ricci, F.: Optimal radio channel recommendations with explicit and implicit feedback. In: P. Cunningham, N.J. Hurley, I. Guy, S.S. Anand (eds.) Sixth ACM Conference on Recommender Systems, RecSys ’12, Dublin, Ireland, September 9–13, 2012, pp. 75–82. ACM (2012). DOI 10.1145/2365952.2365971. URL http://doi.acm.org/10.1145/2365952.2365971

  67. Odic, A., Tkalcic, M., Tasic, J.F., Kosir, A.: Predicting and detecting the relevant contextual information in a movie-recommender system. Interacting with Computers 25(1), 74–90 (2013). DOI 10.1093/iwc/iws003. URL http://dx.doi.org/10.1093/iwc/iws003

  68. Oku, K., Nakajima, S., Miyazaki, J., Uemura, S.: Context-aware SVM for context-dependent information recommendation. In: Proceedings of the 7th International Conference on Mobile Data Management, p. 109 (2006)

    Google Scholar 

  69. Palmisano, C., Tuzhilin, A., Gorgoglione, M.: Using context to improve predictive modeling of customers in personalization applications. IEEE Transactions on Knowledge and Data Engineering 20(11), 1535–1549 (2008)

    Article  Google Scholar 

  70. Panniello, U., Tuzhilin, A., Gorgoglione, M.: Comparing context-aware recommender systems in terms of accuracy and diversity. User Model. User-Adapt. Interact. 24(1-2), 35–65 (2014)

    Article  Google Scholar 

  71. Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., Pedone, A.: Experimental comparison of pre-vs. post-filtering approaches in context-aware recommender systems. In: Proceedings of the 3rd ACM conference on Recommender systems, pp. 265–268. ACM (2009)

    Google Scholar 

  72. Park, H.S., Yoo, J.O., Cho, S.B.: A context-aware music recommendation system using fuzzy bayesian networks with utility theory. In: Proceedings of the Third International Conference on Fuzzy Systems and Knowledge Discovery, FSKD’06, pp. 970–979. Springer-Verlag, Berlin, Heidelberg (2006). DOI 10.1007/11881599_121. URL http://dx.doi.org/10.1007/11881599_121

  73. Park, M.H., Hong, J.H., Cho, S.B.: Location-based recommendation system using bayesian user’s preference model in mobile devices. In: Proceedings of the 4th International Conference on Ubiquitous Intelligence and Computing, UIC’07, pp. 1130–1139. Springer-Verlag, Berlin, Heidelberg (2007). URL http://dl.acm.org/citation.cfm?id=2391319.2391438

  74. Pennock, D.M., Horvitz, E.: Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. In: IJCAI’99 Workshop: Machine Learning for Information Filtering (1999)

    Google Scholar 

  75. Ramakrishnan, R., Gehrke, J.: Database Management Systems. USA: McGraw Hill Companies (2000)

    Google Scholar 

  76. Reddy, S., Mascia, J.: Lifetrak: Music in tune with your life. In: Proceedings of the 1st ACM International Workshop on Human-centered Multimedia, HCM ’06, pp. 25–34. ACM, New York, NY, USA (2006). DOI 10.1145/1178745.1178754. URL http://doi.acm.org/10.1145/1178745.1178754

  77. Ricci, F.: Mobile recommender systems. J. of IT & Tourism 12(3), 205–231 (2010)

    Google Scholar 

  78. Ricci, F., Nguyen, Q.N.: Mobyrek: A conversational recommender system for on-the-move travelers. Destination Recommendation Systems: Behavioural Foundations and Applications pp. 281–294 (2006)

    Google Scholar 

  79. Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer (2011)

    Google Scholar 

  80. Sae-Ueng, S., Pinyapong, S., Ogino, A., Kato, T.: Personalized shopping assistance service at ubiquitous shop space. In: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops, AINAW ’08, pp. 838–843. IEEE Computer Society, Washington, DC, USA (2008). DOI 10.1109/WAINA.2008.287. URL http://dx.doi.org/10.1109/WAINA.2008.287

  81. Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp. 285–295. ACM (2001)

    Google Scholar 

  82. Sarwat, M., Avery, J., Mokbel, M.F.: A recdb in action: Recommendation made easy in relational databases. PVLDB 6(12), 1242–1245 (2013)

    Google Scholar 

  83. Sarwat, M., Levandoski, J.J., Eldawy, A., Mokbel, M.F.: Lars*: An efficient and scalable location-aware recommender system. IEEE Trans. Knowl. Data Eng. 26(6), 1384–1399 (2014)

    Article  Google Scholar 

  84. Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci et al. [79], pp. 257–297

    Google Scholar 

  85. Smyth, B., Cotter, P.: Mp3 mobile portals, profiles and personalization. In: Web Dynamics, pp. 411–433. Springer Berlin Heidelberg (2004). DOI 10.1007/978-3-662-10874-1_17. URL http://dx.doi.org/10.1007/978-3-662-10874-1_17

  86. Van Setten, M., Pokraev, S., Koolwaaij, J.: Context-aware recommendations in the mobile tourist application COMPASS. In: W. Nejdl, P. De Bra (eds.) Adaptive Hypermedia, pp. 235–244. Springer Verlag (2004)

    Google Scholar 

  87. Webster, N.: Webster’s new twentieth century dictionary of the English language. Springfield, MA: Merriam-Webster, Inc. (1980)

    Google Scholar 

  88. Woerndl, W., Huebner, J., Bader, R., Gallego-Vico, D.: A model for proactivity in mobile, context-aware recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ’11, pp. 273–276. ACM, New York, NY, USA (2011). DOI 10.1145/2043932.2043981. URL http://doi.acm.org/10.1145/2043932.2043981

  89. Yu, Z., Zhou, X., Zhang, D., Chin, C.Y., Wang, X., Men, J.: Supporting context-aware media recommendations for smart phones. IEEE Pervasive Computing 5(3), 68–75 (2006)

    Article  Google Scholar 

  90. Zheng, Y., Burke, R., Mobasher, B.: Differential context relaxation for context-aware travel recommendation. In: C. Huemer, P. Lops (eds.) E-Commerce and Web Technologies, Lecture Notes in Business Information Processing, vol. 123, pp. 88–99. Springer Berlin Heidelberg (2012). DOI 10.1007/978-3-642-32273-0_8. URL http://dx.doi.org/10.1007/978-3-642-32273-0_8

Download references

Acknowledgements

Research of A. Tuzhilin was supported in part by the National Science Foundation grant IIS-1256036, USA.

Author information

Authors and Affiliations

  1. Department of Information and Decision Sciences, University of Minnesota, 321 19th Avenue South, Minneapolis, MN, 55455, USA

    Gediminas Adomavicius

  2. Department of Information, Operations and Management Sciences, Stern School of Business, New York University, 44 West 4th Street, Rm 8-85, New York, NY, 10012, USA

    Alexander Tuzhilin

Authors
  1. Gediminas Adomavicius
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Tuzhilin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gediminas Adomavicius .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this chapter

Cite this chapter

Adomavicius, G., Tuzhilin, A. (2015). Context-Aware Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7637-6_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4899-7636-9

  • Online ISBN: 978-1-4899-7637-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation