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Hybreed: A software framework for developing context-aware hybrid recommender systems

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Abstract

This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call dynamic contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed’s functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.

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References

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

    Article  Google Scholar 

  • Adomavicius G., Tuzhilin A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  • Adomavicius G., Tuzhilin A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds) Recommender Systems Handbook, pp. 217–253. Springer, Berlin (2010)

    Google Scholar 

  • Adomavicius G., Sankaranarayanan R., Sen S., Tuzhilin A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23(1), 103–145 (2005)

    Article  Google Scholar 

  • Albert, W., Dixon, E.: Is this what you expected? The use of expectation measures in usability testing. In: UPA ’03: Proceedings of the Usability Professionals Association 2003, Scottsdale (2003)

  • Anand, S., Mobasher, B.: Contextual recommendation. In: Berendt, B., Hotho, A., Mladenic, D., Semeraro, G. (eds.) From Web to Social Web: Discovering and Deploying User and Content Profiles, pp. 142–160. Springer, Berlin (2007)

  • Assad, M., Carmichael, D.J., Kay, J., Kummerfeld, B.: PersonisAD: distributed, active, scrutable model framework for context-aware services. In: PERVASIVE ’07: Proceedings of the 5th International Conference on Pervasive Computing, pp. 55–72. Springer, Berlin (2007)

  • Balabanovic M., Shoham Y.: Combining content-based and collaborative recommendation. Commun. ACM 40, 66–72 (1997)

    Article  Google Scholar 

  • Baldauf M., Dustdar S., Rosenberg F.: A survey on context-aware systems. Int. J. Ad Hoc Ubiquitous Comput. 2(4), 263–277 (2007)

    Article  Google Scholar 

  • Baltrunas, L., Ricci, F.: Experimental evaluation of context-dependent collaborative filtering using item splitting. User Model. User-Adap. Inter. (this issue) (2013)

  • Bangor A., Kortum P., Miller J.: Determining what individual SUS scores mean: adding an adjective rating scale. J. Usability Stud. 4(3), 114–123 (2009)

    Google Scholar 

  • Bazire, M., Brézillon, P.: Understanding context before using it. In: Dey A.K., Kokinov B.N., Leake D.B., Turner R.M. (eds.) CONTEXT ’05: Proceedings of the 5th International and Interdisciplinary Conference on Context, Lecture Notes in Computer Science, vol. 3554, pp. 29–40. Springer, Heidelberg (2005)

  • Beliakov G., Calvo T., James S.: Aggregation of preferences in recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds) Recommender Systems Handbook, pp. 705–734. Springer, Berlin (2010)

    Google Scholar 

  • Berkovsky, S., Freyne, J.: Group-based recipe recommendations: analysis of data aggregation strategies. In: RecSys ’10: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 111–118. ACM, New York (2010)

  • Brooke J.: SUS—A Quick and Dirty Usability Scale. Usability Evaluation in Industry. Taylor & Francis, London (1996)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  • Burke, R.: Hybrid web recommender systems. In: Brusilovsky P., Kobsa A., Nejdl W. (eds.) The Adaptive Web. Methods and Strategies of Web Personalization, Lecture Notes in Computer Science, vol. 4321, pp. 377–408. Springer, Berlin (2007)

  • Carmagnola F., Cena F., Console L., Cortassa O., Gena C., Goy A., Torre I., Toso A., Vernero F.: Tag-based user modeling for social multi-device adaptive guides. User Model. User-Adap. Inter. 18(5), 497–538 (2008)

    Article  Google Scholar 

  • Carmagnola F., Cena F., Gena C.: User model interoperability: a survey. User Model. User-Adap. Inter. 21(3), 1–47 (2011)

    Article  Google Scholar 

  • Chen, H.: An intelligent broker architecture for pervasive context-aware systems. Ph.D. thesis, University of Maryland, Baltimore (2004)

  • Chi, E.: Transient user profiling. In: Proceedings of the Workshop on User Profiling (held in conjunction with CHI 2004), Vienna (2004)

  • Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Proceedings of ACM SIGIR Workshop on Recommender Systems. ACM, New York (1999)

  • Collins A.M., Loftus E.F.: A spreading activation theory of semantic processing. Psychol. Rev. 82(6), 407–428 (1975)

    Article  Google Scholar 

  • Costa, P.D.: Architectural support for context-aware applications: From context models to service platforms. Ph.D. thesis, University of Twende, Enschede (2007)

  • Crestani F.: Application of spreading activation techniques in information retrieval. Artif. Intell. Rev. 11(6), 453–482 (1997)

    Article  Google Scholar 

  • Crossen, A., Budzik, J., Hammond, K.: Flytrap: intelligent group music recommendation. In: IUI ’02: Proceedings of the 7th International Conference on Intelligent User Interfaces, pp. 184–185. ACM, New York (2002)

  • Dey, A.K.: Context-aware computing: the cyberdesk project. In: Proceedings of the AIII 1998 Spring Symposium on Intelligent Environments, pp. 51–54. AIII Press, Palo Alto (1998)

  • Dey A.K.: Understanding and using context. Pers. Ubiquitous Comput. 5(1), 4–7 (2001)

    Article  Google Scholar 

  • Dey, A.K., Abowd, G.D.: Towards a better understanding of context and context-awareness. In: Proceedings of the CHI 2000 Workshop on the What, Who, Where, When, and How of Context-Awareness. ACM Press, The Hague (2000)

  • Dey, A.K., Abowd, G.D., Wood, A.: Cyberdesk: a framework for providing self-integrating context-aware services. In: IUI ’98: Proceedings of the International Conference on Intelligent User Interfaces, pp. 47–54. ACM, New York (1998)

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

    Google Scholar 

  • Dix, A., Katifori, A., Lepouras, G., Vassilakis, C., Shabir, N.: Spreading activation over ontology-based resources: from personal context to web scale reasoning. Int. J. Semant. Comput. Special issue on web scale reasoning: scalable, tolerant and dynamic, 4(1):59–102 (2010)

    Google Scholar 

  • Dourish P.: What we talk about when we talk about context. Pers. Ubiquitous Comput 8(1), 19–30 (2004)

    Article  Google Scholar 

  • Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: WWW ’01: Proceedings of the 10th International Conference on World Wide Web, WWW ’01, pp. 613–622. ACM, New York (2001)

  • Ekstrand, M.D., Ludwig, M., Konstan, J.A., Riedl, J.: Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. In: RecSys ’11: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 133–140. ACM, New York (2011)

  • Esposito A., Tarricone L., Zappatore M., Catarinucci L., Colella R.: A framework for context-aware home-health monitoring. Int. J. Auton. Adapt. Commun. Syst. 3(1), 75–91 (2010)

    Article  Google Scholar 

  • Freyne, J., Smyth, B.: Cooperating search communities. In: Wade V., Ashman H., Smyth B. (eds.) AH ’06: Adaptive Hypermedia and Adaptive Web-Based Systems, Lecture Notes in Computer Science, vol. 4018, pp. 101–110. Springer, Berlin (2006)

  • Gantner, Z., Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Mymedialite: a free recommender system library. In: RecSys ’11: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 305–308. ACM, New York (2011)

  • Garcia, I., Sebastia, L., Onaindia, E., Guzman, C.: A group recommender system for tourist activities. In: Noia T.D., Buccafurri F. (eds.) E-Commerce and Web Technologies, Lecture Notes in Computer Science, vol. 5692, pp. 26–37. Springer, Berlin (2009)

  • Goodwin, C., Duranti, A.: Rethinking context: an introduction. In: Duranti A., Goodwin C. (eds.) Rethinking Context: Language as an Interactive Phenomenon, No. 11 in Studies in the Social and Cultural Foundation of Language. Cambridge University Press, Cambridge (1992)

  • Haake J., Hussein T., Joop B., Lukosch S., Veiel D., Ziegler J.: Modeling and exploiting context for adaptive collaboration. Int. J. Coop. Inf. Syst. 19(1-2), 71–120 (2010)

    Article  Google Scholar 

  • Han, E.H., Karypis, G.: Feature-based recommendation system. In: CIKM ’05: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 446–452. ACM, New York (2005)

  • Heckmann, D.: Ubiquitous user modeling. Ph.D. thesis, Saarland University, Saarbruecken (2005)

  • Henricksen, K., Indulska, J., McFadden, T., Balasubramaniam, S.: Middleware for distributed context-aware systems. In: DOA ’10: Proceedings of the International Symposium on Distributed Objects and Applications, No. 3760 in Lecture Notes in Computer Science, pp. 846–863. Springer, Berlin (2005)

  • Herlocker J.L., Konstan J.A.: Content-independent task-focused recommendation. IEEE Internet Comput. 5(6), 40–47 (2001)

    Article  Google Scholar 

  • Hofer, T., Schwinger, W., Pichler, M., Leonhartsberger, G., Altmann, J., Retschitzegger, W.: Context-awareness on mobile devices—the Hydrogen approach. In: HICSS ’03: Proceedings of the 36th Annual Hawaii International Conference on System Sciences. IEEE, Washington, DC (2003)

  • Hussein, T., Münter, D.: Automated generation of a faceted navigation interface using semantic models. In: Hussein T., Ziegler J., Lukosch S., Dix A. (eds.) SEMAIS ’10: Proceedings of the 1st Workshop on Semantic Models for Adaptive Interactive Systems. ACM, New York (2010)

  • Jameson, A.: More than the sum of its members: challenges for group recommender systems. In: Proceedings of the International Working Conference on Advanced Visual Interfaces, Gallipoli, pp. 48–54 (2004)

  • Jameson, A., Smyth, B.: Recommendation to groups. In: Brusilovsky P., Kobsa A., Nejdl W. (eds.) The Adaptive Web. Methods and Strategies of Web Personalization, Lecture Notes in Computer Science, vol. 4321, pp. 596–627. Springer, Berlin (2007)

  • Jannach D., Zanker M., Felfernig A., Friedrich G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2010)

    Book  Google Scholar 

  • Jin, X., Zhou, Y., Mobasher, B.: Task-oriented web user modeling for recommendation. In: UM ’05: Proceedings of the 10th International Conference on User Modeling, Lecture Notes in Computer Science, vol. 3538, pp. 109–118. Springer, Berlin (2005)

  • Kaltz, J.W.: An engineering method for adaptive, context-aware web applications. Ph.D. thesis, University of Duisburg-Essen, Essen (2006)

  • Kaminskas, M., Ricci, F.: Location-adapted music recommendation using tags. In: Konstan J.A., Marzo J.L., Conejo R., Oliver N. (eds.) UMAP ’11: Proceedings of the 19th International Conference on User Modeling, Adaptation, and Personalization, Girona, pp. 183–194 (2011)

  • Korpipää P., Mantyjarvi J., Kela J., Keranen H., Malm E.J.: Managing context information in mobile devices. IEEE Pervasive Comput. 2, 42–51 (2003)

    Article  Google Scholar 

  • Lim, B.Y., Dey, A.K.: Toolkit to support intelligibility in context-aware applications. In: UBICOMP ’10: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 13–22. ACM, New York (2010)

  • Linden G., Smith B., York J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  • Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: IUI ’10: Proceeding of the 14th International Conference on Intelligent User Interfaces, pp. 31–40, ACM, New York (2010)

  • Ma H., Zhou T.C., Lyu M.R., King I.: Improving recommender systems by incorporating social contextual information. ACM Trans. Inf. Syst. 29(2), 1–23 (2011)

    Article  Google Scholar 

  • Masthoff J.: Group modeling: selecting a sequence of television items to suit a group of viewers. User Model. User-Adap. Inter. 14(1), 37–85 (2004)

    Article  Google Scholar 

  • Masthoff J.: Group recommender systems: combining individual models. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds) Recommender Systems Handbook, pp. 677–702. Springer, Berlin (2010)

    Google Scholar 

  • Masthoff J., Gatt A.: In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Model. User-Adap. Inter. 16(3–4), 281–319 (2006)

    Article  Google Scholar 

  • Mayrhofer, R.: An architecture for context prediction. Ph.D. thesis, Johannes Kepler University of Linz, Linz (2004)

  • McCarthy, J.F., Anagnost, T.D.: Musicfx: an arbiter of group preferences for computer supported collaborative workouts. In: CSCW ’98: Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work, pp. 363–372. ACM, New York (1998)

  • Microsoft (ed.): Microsoft Application Architecture Guide. Microsoft Press, Redmond (2009)

  • Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM Conference on Digital Libraries, ACM, New York, NY, USA, pp. 195–204 (2000)

  • O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: Polylens: a recommender system for groups of users. In: ECSCW’01: Proceedings of the 7th Conference on European Conference on Computer Supported Cooperative Work, pp. 199–218. Kluwer, Norwell (2001)

  • Palmisano C., Tuzhilin A., Gorgoglione M.: Using context to improve predictive modeling of customers in personalization applications. IEEE Trans. Knowl. Data Eng. 20(11), 1535–1549 (2008)

    Article  Google Scholar 

  • Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., Pedone, A.: Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In: RecSys ’09: Proceedings of the 3rd ACM Conference on Recommender Systems, pp. 265–268. ACM, New York (2009)

  • Panniello, U., Tuzhilin, A., Gorgoglione, M.: Comparing context-aware recommender systems in terms of accuracy and diversity: which contextual modeling, pre-filtering and post-filtering methods perform the best. User Model. User-Adap. Inter. (this issue) (2013)

  • Pascoe, J.: Adding generic contextual capabilities to wearable computers. In: ISWC’98: Proceedings of the 2nd International Symposium on Wearable Computers, pp. 92–99. IEEE Computer Society, Los Alamitos (1998)

  • Pazzani M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5-6), 393–408 (1999)

    Article  Google Scholar 

  • Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW ’94: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM, New York (1994)

  • Ricci F., Rokach L., Shapira B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds) Recommender Systems Handbook, pp. 1–35. Springer, Berlin (2010)

    Google Scholar 

  • Roman M., Hess C., Cerqueira R., Ranganathan A., Campbell R.H., Nahrstedt K.: Gaia: a middleware platform for active spaces. ACM SIGMOBILE Mobile Comput. Commun. Rev. 6(4), 65–67 (2002)

    Article  Google Scholar 

  • Sørensen, C.F., Wu, M., Sivaharan, T., Blair, G.S., Okanda, P., Friday, A., Duran-Limon, H.: Mpac ’04: a context-aware middleware for applications in mobile ad hoc environments. In: Proceedings of the 2nd Workshop on Middleware for Pervasive and Ad-Hoc Computing, pp. 107–110. ACM, New York (2004)

  • Strang, T., Linnhoff-Popien, C.: A context modeling survey. In: Workshop on Advanced Context Modelling, Reasoning and Management as part of the 6th International Conference on Ubiquitous Computing (UbiComp 2004), Nottingham (2004)

  • van Setten, M.: Supporting people in finding information: hybrid recommender systems and goal-based structuring. Ph.D. thesis, University of Twente, Enschede (2005)

  • Winograd T.: Architectures for context. Hum-Comput. Interact. 16(2), 401–419 (2001)

    Article  Google Scholar 

  • Yau S.S., Karim F.: A context-sensitive middleware-based approach to dynamically integrating mobile devices into computational infrastructures. J. Parallel Distrib. Comput. 64(2), 301–317 (2004)

    Article  Google Scholar 

  • Yau S.S., Karim F., Wang Y., Wang B., Gupta S.K.S.: Reconfigurable context-sensitive middleware for pervasive computing. IEEE Pervasive Comput. 1(3), 33–40 (2002)

    Article  Google Scholar 

  • Zadeh L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  • Zimmermann, A., Lorenz, A., Oppermann, R.: An operational definition of context. In: Kokinov B., Brézillon P., Petkov G. (eds.) Context ’07: Proceedings of 6th International and Interdisciplinary Conference on Modeling and Using Context, Lecture Notes in Computer Science, pp. 558–571. Springer, Berlin (2007)

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Hussein, T., Linder, T., Gaulke, W. et al. Hybreed: A software framework for developing context-aware hybrid recommender systems. User Model User-Adap Inter 24, 121–174 (2014). https://doi.org/10.1007/s11257-012-9134-z

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