Skip to main content

A Context-Aware Shopping Portal Based on Semantic Models

  • Chapter

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

This chapter illustrates how semantic models can be used as a backend data source for both exploration and adaptation purposes. For a fictitious shopping portal, we implemented a faceted navigation approach that provides means for exploring the portal’s content manually. In addition to that, we implemented an adaptation mechanism based on spreading activation that also exploits the semantic structure of the underlying data.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   54.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Notes

  1. 1.

    The authors of this chapter co-authored that publication as well. Some of the ideas presented in this chapter have already been introduced in there.

  2. 2.

    Further information on the sensing mechanism applied in Discovr can be found in a different publication by the authors (Hussein et al. 2013).

References

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

    Article  Google Scholar 

  • Adomavicius, G., & Tuzhilin, A. (2005). 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.

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • Anand, S., & Mobasher, B. (2007). Contextual recommendation. In B. Berendt, A. Hotho, D. Mladenic & G. Semeraro (Eds.), From web to social web: discovering and deploying user and content profiles (pp. 142–160). Berlin: Springer.

    Chapter  Google Scholar 

  • Anderson, J. R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22, 261–295.

    Article  Google Scholar 

  • Balabanovic, M., & Shoham, Y. (1997). Combining content-based and collaborative recommendation. Communications of the ACM, 40, 66–72.

    Article  Google Scholar 

  • Baltrunas, L., & Ricci, F. (2013). Experimental evaluation of context-dependent collaborative filtering using item splitting. User Modeling and User-Adapted Interaction. doi:10.1007/s11257-012-9137-9.

    Google Scholar 

  • Berger, H., Dittenbach, M., & Merkl, D. (2004). An adaptive information retrieval system based on associative networks. In APCCM ’04: proceedings of the 1st Asian-Pacific conference on conceptual modeling (pp. 27–36). Darlinghurst: Australian Computer Society.

    Google Scholar 

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

    Article  MATH  Google Scholar 

  • Burke, R. (2007). Hybrid web recommender systems. In P. Brusilovsky, A. Kobsa & W. Nejdl (Eds.), Lecture notes in computer science: Vol. 4321. The adaptive web. Methods and strategies of web personalization (pp. 377–408). Berlin: Springer.

    Chapter  Google Scholar 

  • Carmagnola, F., Cena, F., Console, L., Cortassa, O., Gena, C., Goy, A., et al. (2008). Tag-based user modeling for social multi-device adaptive guides. User Modeling and User-Adapted Interaction, 18(5), 497–538.

    Article  Google Scholar 

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

    Google Scholar 

  • Cohen, P. R., & Kjeldsen, R. (1987). Information retrieval by constrained spreading activation in semantic networks. Information Processing & Management, 23(4), 255–268.

    Article  Google Scholar 

  • Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Psychological Review, 82(6), 407–428.

    Article  Google Scholar 

  • Crestani, F. (1997). Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 11(6), 453–482.

    Article  Google Scholar 

  • Davidson, J., Liebald, B., Liu, J., Nandy, P., van Vleet, T., Gargi, U., et al. (2010). The YouTube video recommendation system. In RecSys ’10: proceedings of the 4th ACM conference on recommender systems (pp. 293–296). New York: ACM.

    Chapter  Google Scholar 

  • Freyne, J., Berkovsky, S., Daly, E. M., & Geyer, W. (2010). Social networking feeds: recommending items of interest. In RecSys ’10: proceedings of the 4th ACM conference on recommender systems (pp. 277–280). New York: ACM.

    Chapter  Google Scholar 

  • Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1993). Design patterns: abstraction and reuse in object-oriented designs. In ECOOP ’93: proceedings of the 7th European conference on object-oriented programming. Berlin: Springer.

    Google Scholar 

  • Gibbins, N., Harris, S., Dix, A., & Schraefel, M. C. (2003). Electronics and computer science: Vol. 8639. Applying mSpace interfaces to the semantic web. Southampton: University of Southampton.

    Google Scholar 

  • Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61–70.

    Article  Google Scholar 

  • Han, E.-H., & Karypis, G. (2005). Feature-based recommendation system. In CIKM ’05: proceedings of the 14th ACM international conference on information and knowledge management (pp. 446–452). New York: ACM. ISBN: 1-59593-140-6.

    Chapter  Google Scholar 

  • Heim, P., Ziegler, J., & Lohmann, S. (2008). Gfacet: a browser for the web of data. In S. Auer, S. Dietzold, S. Lohmann & J. Ziegler (Eds.), IMC-SSW’08: proceedings of the international workshop on interacting with multimedia content in the social semantic web (pp. 49–58).

    Google Scholar 

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

    Article  Google Scholar 

  • Hildebrand, M., van Ossenbruggen, J. R., & Hardman, L. (2006). Gfacet: a browser for heterogeneous semantic web repositories. In ISWC ’06: proceedings of the 5th international semantic web conference (pp. 272–285). Berlin: Springer.

    Chapter  Google Scholar 

  • Hussein, T. (2010). Interfaces and interaction design for learning and simulation environments. In N. Baloian, W. Luther, D. Söffker & Y. Urano (Eds.), Context-aware recommendations. Berlin: Logos.

    Google Scholar 

  • Hussein, T., & Gaulke, W. (2010). Hybride, kontext-sensitive Generierung von Produktempfehlungen. i-com. Zeitschrift für interaktive und kooperative Medien, 9(2), 16–23.

    Google Scholar 

  • Hussein, T., & Münter, D. (2010). Automated generation of a faceted navigation interface using semantic models. In T. Hussein, J. Ziegler, S. Lukosch & A. Dix (Eds.), SEMAIS ’10: proceedings of the 1st workshop on semantic models for adaptive interactive systems.

    Chapter  Google Scholar 

  • Hussein, T., & Neuhaus, S. (2010). Explanation of spreading activation based recommendations. In T. Hussein, J. Ziegler, S. Lukosch & A. Dix (Eds.), SEMAIS ’10: proceedings of 1st workshop on semantic models for adaptive interactive systems.

    Chapter  Google Scholar 

  • Hussein, T., & Ziegler, J. (2008). Adapting web sites by spreading activation in ontologies. ReColl ’08: proceedings of the international workshop on recommendation and collaboration. New York: ACM.

    Google Scholar 

  • Hussein, T., & Ziegler, J. (2010). Situationsgerechtes recommending. Informatik Spektrum, 34(2), 143–152.

    Article  Google Scholar 

  • Hussein, T., Westheide, D., & Ziegler, J. (2007). Context-adaptation based on ontologies and spreading activation. In I. Brunkhorst, D. Krause & W. Sitou (Eds.), Proceedings of ABIS ’07: 15th workshop on adaptivity and user modeling in interactive systems.

    Google Scholar 

  • Hussein, T., Linder, T., Gaulke, W., & Ziegler, J. (2009). Context-aware recommendations on rails. CARS ’ 09: proceedings of the 1st workshop on context-aware in recommender systems. New York.

    Google Scholar 

  • Hussein, T., Linder, T., Gaulke, W., & Ziegler, J. (2010a). A framework and an architecture for context-aware group recommendations. In G. Kolfschoten, T. Herrmann & S. Lukosch (Eds.), Lecture notes in computer science: Vol. 6257. CRIWG ’10: proceedings of the 16th conference on collaboration and technology (pp. 121–128). Berlin: Springer.

    Chapter  Google Scholar 

  • Hussein, T., Gaulke, W., Linder, T., & Ziegler, J. (2010b). Improving collaboration by using context views. In CAICOLL ’10: proceedings of the 1st workshop on context-adaptive interaction for collaborative work.

    Google Scholar 

  • Hussein, T., Linder, T., Gaulke, W., & Ziegler, J. (2013). Hybreed: A software framework for developing context-aware hybrid recommender systems. User Modeling and User-Adapted Interaction. doi:10.1007/s11257-012-9134-z.

    Google Scholar 

  • Jin, X., Zhou, Y., & Mobasher, B. (2005). Task-oriented web user modeling for recommendation. In Lecture notes in computer science: Vol. 3538. UM ’05: proceedings of the 10th international conference on user modeling (pp. 109–118).

    Google Scholar 

  • Kaminskas, M., & Ricci, F. (2011). Location-adapted music recommendation using tags. In J. A. Konstan, J. L. Marzo, R. Conejo & N. Oliver (Eds.), UMAP ’11: proceedings of the 19th international conference on user modeling, adaptation, and personalization (pp. 183–194).

    Chapter  Google Scholar 

  • Kim, S., & Kwon, J. (2007). Effective context-aware recommendation on the semantic web. International Journal of Computer Science and Network Security, 7(8), 154–159.

    MathSciNet  Google Scholar 

  • Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010). A contextual-bandit approach to personalized news article recommendation. In WWW ’10: proceedings of the 19th international conference on world wide web.

    Google Scholar 

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

    Article  Google Scholar 

  • Loizou, A., & Dasmahapatra, S. (2006). Recommender systems for the semantic web. In ECAI 06: recommender systems workshop.

    Google Scholar 

  • Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., & Koper, R. (2010). Recommender systems in technology enhanced learning. In F. Ricci, L. Rokach, B. Shapira & P. B. Kantor (Eds.), Recommender systems handbook (pp. 387–415). Berlin: Springer.

    Google Scholar 

  • Middleton, S. E., Shadbolt, N. R., & de Roure, D. C. (2004). Ontological user profiling in recommender systems. ACM Transactions on Information Systems, 22(1), 54–88.

    Article  Google Scholar 

  • Mobasher, B., Jin, X., & Zhou, Y. (2004). Semantically enhanced collaborative filtering on the web. In B. Berendt, A. Hotho, D. Mladenic, M. van Someren Myra Spiliopoulou & G. Stumme (Eds.), Lecture notes in computer science: Vol. 3209. Web mining: from web to semantic web. Berlin: Springer.

    Google Scholar 

  • Mooney, R. J., & Roy, L. (2000). Content-based book recommending using learning for text categorization. In Proceedings of the 5th ACM conference on digital libraries (pp. 195–204). New York: ACM. ISBN: 1-58113-231-X.

    Chapter  Google Scholar 

  • Oren, E., Delbru, R., & Decker, S. (2006). Extended faceted navigation for RDF data. In ISWC ’06: proceedings of the 5th international semantic web conference (pp. 559–572).

    Chapter  Google Scholar 

  • Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5–6), 393–408.

    Article  Google Scholar 

  • Pirolli, P., & Card, S. (1995). Information foraging in information access environments. In I. R. Katz, R. Mack, L. Marks, M. B. Rosson & J. Nielsen (Eds.), CHI ’95: proceedings of the 1995 SIGCHI conference on human factors in computing systems (pp. 51–58). Denver: ACM.

    Chapter  Google Scholar 

  • Plaisant, C., Shneiderman, B., Doan, K., & Bruns, T. (1999). Interface and data architecture for query preview in networked information systems. ACM Transactions on Information Systems, 17(3), 320–341.

    Article  Google Scholar 

  • Quan, D., Huynh, D., & Karger, D. R. (2003). Haystack: a platform for authoring end user semantic web applications. In ICSW ’06: proceedings of the 2nd international semantic web conference (pp. 738–753). Berlin: Springer.

    Google Scholar 

  • Ranganathan, S. R. (1962). Elements of library classification. Bombay: Asia Publishing House.

    Google Scholar 

  • Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). 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). New York: ACM. ISBN: 0-89791-689-1.

    Chapter  Google Scholar 

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

    Google Scholar 

  • Rich, E. (1979). User modeling via stereotypes. Cognitive Science, 3(4), 329–354.

    Article  Google Scholar 

  • Salton, G., & Buckley, C. (1988). On the use of spreading activation methods in automatic information retrieval. In Y. Chiaramella (Ed.), Proceedings of the 11th annual international ACM SIGIR conference on research and development in information retrieval (pp. 147–160). New York: ACM.

    Google Scholar 

  • Sarwar, B., Karypis, G., Konstan, J. A., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In V. Y. Shen, N. Saito, M. R. Lyu & M. E. Zurko (Eds.), WWW ’11: proceedings of the 10th international conference on world wide web (pp. 285–295). Hong Kong: ACM. ISBN: 1-58113-348-0.

    Chapter  Google Scholar 

  • Sieg, A., Mobasher, B., & Burke, R. (2010). Improving the effectiveness of collaborative recommendation with ontology-based user profiles. In HetRec ’10: proceedings of the 1st international workshop on information heterogeneity and fusion in recommender systems (pp. 39–46). New York: ACM.

    Chapter  Google Scholar 

  • Stevens, S. S. (1946). On the theory of scales of measurement. Science, 193(2684), 677–680.

    Article  Google Scholar 

  • Yee, K.-P., Swearingen, K., Li, K., & Hearst, M. (2003). Faceted metadata for image search and browsing. In CHI ’03: proceedings of the 2003 SIGCHI conference on human factors in computing systems (pp. 401–408). New York: ACM. ISBN: 1-58113-630-7.

    Chapter  Google Scholar 

Download references

Acknowledgements

Discovr and several predecessors that have been implemented over the years, have been mentioned in publications by the authors: Hussein and Ziegler 2008, 2010, Hussein and Gaulke 2010, Hussein and Neuhaus 2010, Hussein and Münter 2010, Hussein 2010, Hussein et al. 2007, 2009, 2010a, 2010b, 2013.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tim Hussein .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Hussein, T., Linder, T., Ziegler, J. (2013). A Context-Aware Shopping Portal Based on Semantic Models. In: Hussein, T., Paulheim, H., Lukosch, S., Ziegler, J., Calvary, G. (eds) Semantic Models for Adaptive Interactive Systems. Human–Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-1-4471-5301-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-5301-6_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5300-9

  • Online ISBN: 978-1-4471-5301-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics