Cross Domain Recommendation System Using Ontology and Sequential Pattern Mining

  • S. UdayambihaiEmail author
  • V. UmaEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Recommendation system is very helpful to filter the information according to the user interest and provide user personalized suggestion. Recommendation system is emerging now-a-days in many social networks like Facebook, Twitter, e-commerce etc. Cross domain recommendation system is one of the method to develop the recommendation where we can gather the knowledge from different domains and recommend most similar items related to the user search term. In this work, we try to extend cross domain recommendation by finding semantic similarity of items in Ontology, applying Collaborative Filtering and recommending user preferred items using PrefixSpan algorithm. The similarity between items can be achieved through modified Wpath method. Finally, we can recommend the most preferred items and evaluate using performance measures like F-score.


Semantic similarity Ontology Cross domain recommendation Collaborative filtering Prefix span 


  1. 1.
    Kumar A, Kumar N, Hussain M, Chaudhury S, Agarwal S (2015) Semantic clustering-based cross-domain recommendation. In: IEEE SSCI 2014–2014 IEEE symposium series on computational intelligence—CIDM 2014: 2014 IEEE symposium on computational intelligence and data mining, proceedings, pp 137–141.
  2. 2.
    Al-Nazer A, Helmy T (2015) Personalizing health and food advices by semantic enrichment of multilingual cross-domain questions. In: 2015 IEEE 8th GCC conference and exhibition, GCCCE 2015, pp 1–4.
  3. 3.
    Kumar V, Shrivastva KMP, Singh S (2016) Cross domain recommendation using semantic similarity and tensor decomposition. Procedia Comput Sci 85(Cms):317–324. Scholar
  4. 4.
    Xu Z, Zhang F, Wang W, Liu H, Kong X (2016) Exploiting trust and usage context for cross-domain recommendation. IEEE Access 4:2398–2407. Scholar
  5. 5.
    Liu L, Cui J, Song W, Wang H (2017) Multi-domain collaborative recommendation with feature selection. China Commun 14(8):137–148. Scholar
  6. 6.
    Hao P, Zhang G, Lu J (2016) Enhancing cross domain recommendation with domain dependent tags. In: 2016 IEEE international conference on fuzzy systems, Fuzz-IEEE 2016, pp 1266–1273.
  7. 7.
    Thendral SE, Valliyammai C (2017) Clustering based transfer learning in cross domain recommender system. In: 2016 8th international conference on advanced computing, ICoAC 2016, pp 51–54.
  8. 8.
    Zhu G, Iglesias CA (2017) Computing semantic similarity of concepts in knowledge graphs. IEEE Trans Knowl Data Eng 29(1):72–85. Scholar
  9. 9.
    Yu XU, Jiang F, Du J, Gong D (2017) A user-based cross domain collaborative filtering algorithm based on a linear decomposition model. IEEE Access 5.
  10. 10.
    Ali F, Kwak D, Khan P, Ei-Sappagh SHA, Islam SMR, Park D, Kwak KS (2017) Merged ontology and SVM-based information extraction and recommendation system for social robots. IEEE Access 5:12364–12379. Scholar
  11. 11.
    Zhang Q, Haglin D (2016) Semantic similarity between Ontologies at different scales. IEEE/CAA J Automatica Sin 3(2):132–140.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer SciencePondicherry UniversityPondicherryIndia

Personalised recommendations