Skip to main content
SpringerLink
Log in

A novel recommendation system based on semantics and context awareness

  • Published:
Computing Aims and scope Submit manuscript

Abstract

The existing content-based recommendation methods have two major limitations. First, due to the defects of the items and the user model matching algorithms, the recommendation results are very narrow. Second, scant attention is paid to the scenario, making the recommendation system not context-aware. It is essential to improve user satisfaction through high-quality recommendation. In this paper, two state-of-the-art methods are analyzed and extended to enhance recommendation performance. The first method is the context-aware recommender, which integrates context information into the recommendation process. The second method is the semantic analysis-based recommender, which incorporates domain semantics. Despite their compatibility, the challenge is to combine them in a way that will fully exploit their potential. An improved content-based model is proposed in this paper incorporating both semantics and context. Context-aware recommendation is performed to improve sensitivity to the context. Semantic relevance-based instance similarity is computed to address the problem of narrowness. The proposed recommendation system is evaluated using metrics (for instance, recall metric) and paralleled with the current methods grounded on the content. Results demonstrate the superiority of the proposed system in terms of accuracy.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Lekakos G, Caravelas P (2008) A hybrid approach for movie recommendation. Multimed Tools Appl 36(1–2):55–70

    Article  Google Scholar 

  2. Kuo FF, Chiang MF, Shan MK, Lee SY (2005) Emotion-based music recommendation by association discovery from film music. In: ACM international conference on multimedia, 2005, vol 36(4), pp 507–510

  3. Bogers T (2010) Movie recommendation using random walks over the contextual graph. In: Proceedings of international workshop on context, 2010

  4. Unger M, Bar A, Shapira B, Rokach L (2016) Towards latent context-aware recommendation systems. Knowl-Based Syst 104:165–178

    Article  Google Scholar 

  5. Pan W, Ming Z (2017) Collaborative recommendation with multiclass preference context. IEEE Intell Syst 32(2):45–51

    Article  Google Scholar 

  6. Golbeck J (2006) Generating predictive movie recommendations from trust in social networks. Lect Notes Comput Sci 3986:93–104

    Article  Google Scholar 

  7. Viana W, Braga R, Lemos FDA, Souza JMOD, Carmo RAF (2014) Mobile photo recommendation and logbook generation using context-tagged images. IEEE Multimed 21(1):24–34

    Article  Google Scholar 

  8. Said A, Berkovsky S, Luca EWD, Hermanns J (2011) In: Proceedings of the 2nd challenge on context-aware movie recommendation, challenge on context-aware movie recommendation, 2011

  9. Winoto P, Tang TY (2010) The role of user mood in movie recommendations. Expert Syst Appl 37(8):6086–6092

    Article  Google Scholar 

  10. Shi Y, Larson M, Hanjalic A (2010) Mining mood-specific movie similarity with matrix factorization for context-aware recommendation. In: Workshop on context-aware movie recommendation, 2010, vol 4(1), pp 34–40

  11. Choi SM, Ko SK, Han YS (2012) A movie recommendation algorithm based on genre correlations. Expert Syst Appl 39(9):8079–8085

    Article  Google Scholar 

  12. Biancalana C, Gasparetti F, Micarelli A, Miola A, Sansonetti G (2011) Context-aware movie recommendation based on signal processing and machine learning. Chall Context-aware Movie Recomm 2011:5–10

    Google Scholar 

  13. Borg M, Wnuk K, Regnell B, Runeson P (2016) Supporting change impact analysis using a recommendation system: an industrial case study in a safety-critical context. IEEE Trans Softw Eng PP(99):135–151

    Google Scholar 

  14. Ostuni VC, Gentile G, Noia TD, Mirizzi R, Romito D (2013) Mobile movie recommendations with linked data. In: International conference on availability, 2013, vol 8127, pp 400–415

  15. Jeong WH, Kim SJ, Park DS, Jin K (2013) Performance improvement of a movie recommendation system based on personal propensity and secure collaborative filtering. J Inf Process Syst 9(1):157–172

    Article  Google Scholar 

  16. Jakob N, Weber SH, Gurevych I (2009) Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. In: International Cikm workshop on topic-sentiment analysis for mass opinion, 2009, vol 59(1), pp 57–64

  17. Chen CC, Huang TC, Park JJ, Yen NY (2015) Real-time smartphone sensing and recommendations towards context-awareness shopping. Multimed Syst 21(1):61–72

    Article  Google Scholar 

  18. Benini S, Canini L, Leonardi R (2011) A connotative space for supporting movie affective recommendation. IEEE Trans Multimed 13(6):1356–1370

    Article  Google Scholar 

  19. Alhamid MF, Rawashdeh M, Dong H, Hossain MA, Alelaiwi A (2016) RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment. Multimed Syst 22(5):587–601

    Article  Google Scholar 

  20. Chen Q, Aickelin U (2008) Movie recommendation systems using an artificial immune system. Social Science Electronic Publishing, New York

    Google Scholar 

  21. He Q, Pei J, Kifer D, Mitra P, Giles L (2010) Context-aware citation recommendation. In: International conference on world wide web, 2010, pp 421–430

  22. Baltrunas L, Ludwig B, Ricci F (2011) Matrix factorization techniques for context aware recommendation. In: ACM conference on recommender systems, 2011, pp 301–304

  23. Hariri N, Mobasher B, Burke R (2012) Context-aware music recommendation based on latenttopic sequential patterns. In: ACM conference on recommender systems, 2012, pp 131–138

  24. Genuit K, André F (2014) Applicability of measurement procedures in soundscape context—experiences and recommendations. J Acoust Soc Am 135(4):2186

    Article  Google Scholar 

  25. Tang J, Hu X, Gao H, Liu H (2012) Exploiting local and global social context for recommendation. In: International joint conference on artificial intelligence, 2013, pp 2712–2718

  26. Shin D, Lee JW, Yeon J, Lee SG (2009) Context-aware recommendation by aggregating user context. In: IEEE conference on commerce & enterprise computing, 2009, vol 12(1), pp 423–430

  27. Zheng Y, Burke R, Mobasher B (2013) Recommendation with differential context weighting. In: Conference on user modeling, 2013, vol 7899, pp 152–164

  28. Su JH, Yeh HH, Yu PS, Tseng VS (2010) Music recommendation using content and context information mining. IEEE Intell Syst 25(1):16–26

    Article  Google Scholar 

  29. Lee WP, Che K, Huang JY (2014) A smart TV system with body-gesture control, tag-based rating and context-aware recommendation. Knowl-Based Syst 56(2):167–178

    Article  Google Scholar 

  30. Oh Y, Choi A, Woo W (2010) u-BabSang: a context-aware food recommendation system. J Supercomput 54(1):61–81

    Article  Google Scholar 

  31. Liu NN, He L, Zhao M (2013) Social temporal collaborative ranking for context aware movie recommendation. ACM Trans Intell Syst Technol 4(1):1–26

    Google Scholar 

  32. Singh VK, Mukherjee M, Mehta GK (2011) Combining collaborative filtering and sentiment classification for improved movie recommendations. In: International conference on multi-disciplinary trends in artificial intelligence, 2011, vol 7080, pp 38–50

  33. Ruiziniesta A, Jimenezdiaz G, Gomezalbarran M (2014) A semantically enriched context-aware OER recommendation strategy and its application to a computer science OER repository. IEEE Trans Educ 57(4):255–260

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Business, Sichuan Agricultural University, Dujiangyan, 611830, China

    Qin Yang

Authors
  1. Qin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qin Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Q. A novel recommendation system based on semantics and context awareness. Computing 100, 809–823 (2018). https://doi.org/10.1007/s00607-018-0627-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-018-0627-4

Keywords

Mathematics Subject Classification

Search

Navigation