Skip to main content
SpringerLink
Log in

Semantic Context-Aware Recommendation via Topic Models Leveraging Linked Open Data

  • Conference paper
  • First Online:
Web Information Systems Engineering – WISE 2016 (WISE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10041))

Included in the following conference series:

Abstract

Context aware recommendation systems are used to provide personalized recommendations by exploiting contextual situation. They take into account not only user preferences, but also additional relevant information (context). Statistical topic models such as Latent Dirichlet Allocation (LDA) have been extensively used for discovering latent semantic topics in text documents. In this paper, we propose a probabilistic topic model that incorporates user interests, item representation and context information in a single framework. In our approach, the contextual information is represented as a subset of the items feature space which is acquired from the knowledge available in the Linked Open Data (LOD). We use DBpedia, a well-known knowledge base in LOD, to utilize the context information in recommendation. Our proposed recommendation framework computes the conditional probability of each item given the user preferences and the additional context. We use these probabilities as recommendation scores to find top-n items for recommendations. The performed experiments demonstrate the effectiveness of our proposed method and shows that leveraging semantic context from the Linked Open Data can improve the quality of the recommendations.

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 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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://grouplens.org/datasets/movielens/1m/.

  2. 2.

    http://sisinflab.poliba.it/semanticweb/lod/recsys/datasets.

  3. 3.

    http://mallet.cs.umass.edu/.

References

  1. Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999). doi:10.1007/3-540-48157-5_29

    Chapter  Google Scholar 

  2. Aciar, S.: Mining context information from consumers reviews. In: Proceedings of Workshop on Context-Aware Recommender System, vol. 201. ACM (2010)

    Google Scholar 

  3. Agarwal, D., Chen, B.C.: flDA: matrix factorization through latent Dirichlet allocation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 91–100. ACM (2010)

    Google Scholar 

  4. Anand, S.S., Kearney, P., Shapcott, M.: Generating semantically enriched user profiles for web personalization. ACM Trans. Internet Technol. (TOIT) 7(4), 22 (2007)

    Article  Google Scholar 

  5. Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Appl. Artif. Intell. 17(8–9), 687–714 (2003)

    Article  Google Scholar 

  6. Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Pers. Ubiquit. Comput. 16(5), 507–526 (2012)

    Article  Google Scholar 

  7. Baltrunas, L., Ludwig, B., Ricci, F.: Matrix factorization techniques for context aware recommendation. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 301–304. ACM (2011)

    Google Scholar 

  8. Baltrunas, L., Ricci, F.: Context-based splitting of item ratings in collaborative filtering. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 245–248. ACM (2009)

    Google Scholar 

  9. Beutel, A., Murray, K., Faloutsos, C., Smola, A.J.: CoBaFi: collaborative bayesian filtering. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 97–108. ACM (2014)

    Google Scholar 

  10. Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. Semant. Serv. Interoperability Web Appl. Emerg. Concepts 5, 205–227 (2009)

    Google Scholar 

  11. Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia-a crystallization point for the web of data. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 154–165 (2009)

    Article  Google Scholar 

  12. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  13. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)

    Google Scholar 

  14. Cantador, I., Bellogín, A., Castells, P.: A multilayer ontology-based hybrid recommendation model. AI Commun. 21, 203–210 (2008)

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  16. Chen, G., Chen, L.: Recommendation based on contextual opinions. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 61–73. Springer, Heidelberg (2014). doi:10.1007/978-3-319-08786-3_6

    Google Scholar 

  17. 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 

  18. Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D., Zanker, M.: Linked open data to support content-based recommender systems. In: Proceedings of the 8th International Conference on Semantic Systems, pp. 1–8. ACM (2012)

    Google Scholar 

  19. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. USA 101(Suppl 1), 5228–5235 (2004)

    Article  Google Scholar 

  20. Hariri, N., Mobasher, B., Burke, R.: Query-driven context aware recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 9–16. ACM (2013)

    Google Scholar 

  21. Hariri, N., Zheng, Y., Mobasher, B., Burke, R.: Context-aware recommendation based on review mining. General Co-Chairs, p. 27 (2011)

    Google Scholar 

  22. Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: Yago2: a spatially and temporally enhanced knowledge base from Wikipedia. In: Proceedings of the Twenty-Third international Joint Conference on Artificial Intelligence, pp. 3161–3165. AAAI Press (2013)

    Google Scholar 

  23. Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM (1999)

    Google Scholar 

  24. Jahrer, M., Töscher, A., Legenstein, R.: Combining predictions for accurate recommender systems. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 693–702. ACM (2010)

    Google Scholar 

  25. 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, pp. 79–86. ACM (2010)

    Google Scholar 

  26. Leskovec, J., McAuley, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. Department of Computer Science, Stanford University (2013)

    Google Scholar 

  27. Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296. ACM (2011)

    Google Scholar 

  28. Meyers, O.C.: A mood-based music classification and exploration system. Ph.D. thesis, Massachusetts Institute of Technology (2007)

    Google Scholar 

  29. Mobasher, B., Jin, X., Zhou, Y.: Semantically enhanced collaborative filtering on the web. In: Berendt, B., Hotho, A., Mladenič, D., Someren, M., Spiliopoulou, M., Stumme, G. (eds.) EWMF 2003. LNCS (LNAI), vol. 3209, pp. 57–76. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30123-3_4

    Chapter  Google Scholar 

  30. Ostuni, V.C., Di Noia, T., Di Sciascio, E., Mirizzi, R.: Top-n recommendations from implicit feedback leveraging linked open data. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 85–92. ACM (2013)

    Google Scholar 

  31. Passant, A.: dbrec — music recommendations using DBpedia. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6497, pp. 209–224. Springer, Heidelberg (2010). doi:10.1007/978-3-642-17749-1_14

    Chapter  Google Scholar 

  32. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)

    Google Scholar 

  33. Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th International Conference on Machine Learning, pp. 880–887. ACM (2008)

    Google Scholar 

  34. Semeraro, G., Lops, P., Basile, P., de Gemmis, M.: Knowledge infusion into content-based recommender systems. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 301–304. ACM (2009)

    Google Scholar 

  35. Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456. ACM (2011)

    Google Scholar 

  36. Yin, H., Cui, B., Chen, L., Hu, Z., Huang, Z.: A temporal context-aware model for user behavior modeling in social media systems. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 1543–1554. ACM (2014)

    Google Scholar 

  37. Yin, H., Cui, B., Chen, L., Hu, Z., Zhou, X.: Dynamic user modeling in social media systems. ACM Trans. Inf. Syst. (TOIS) 33(3), 10 (2015)

    Article  Google Scholar 

  38. Yin, H., Cui, B., Sun, Y., Hu, Z., Chen, L.: LCARS: a spatial item recommender system. ACM Trans. Inf. Syst. (TOIS) 32(3), 11 (2014)

    Article  Google Scholar 

  39. Yin, H., Zhou, X., Shao, Y., Wang, H., Sadiq, S.: Joint modeling of user check-in behaviors for point-of-interest recommendation. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1631–1640. ACM (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Georgia, Athens, Georgia, USA

    Mehdi Allahyari & Krys Kochut

Authors
  1. Mehdi Allahyari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krys Kochut
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Allahyari .

Editor information

Editors and Affiliations

  1. Poznań University of Economics, Poznan, Poland

    Wojciech Cellary

  2. University of Minnesota, Minneapolis, Minnesota, USA

    Mohamed F. Mokbel

  3. Tsinghua University, Beijing, China

    Jianmin Wang

  4. Victoria University, Melbourne, Victoria, Australia

    Hua Wang

  5. Victoria University, Melbourne, Victoria, Australia

    Rui Zhou

  6. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Allahyari, M., Kochut, K. (2016). Semantic Context-Aware Recommendation via Topic Models Leveraging Linked Open Data. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48740-3_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48739-7

  • Online ISBN: 978-3-319-48740-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation