Skip to main content

Kernel Searching Strategy for Recommender Searching Mechanism

  • Conference paper
  • First Online:
  • 2552 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10603))

Abstract

A trust-aware recommender system (TARS) is widely used in social media to find useful information. Recommender searching mechanism is an important research issue in TARS. We propose a new searching strategy for recommender searching mechanism of TARS, which named kernel searching strategy. A kernel, which consists of hub nodes of the trust network, is involved in trust propagations. The kernel can be obtained from node degree or node betweenness, take these hub nodes as active users and then finds the recommenders via trust propagations from the kernel, most of the nodes in the network will be covered. Comparing the results of these two methods, the coverage rate of these hub nodes which is obtained from the node degree is almost less than that obtained from the node betweenness. To get better coverage rate, we take both degree and betweenness into consideration. The results show that the combination can get better coverage rate only compared with the node degree. However, the combination has better convergence effect compared with the node betweenness.

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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

References

  1. Abbasi, M.A., Tang, J., Liu, H.: Trust-aware recommender systems. In: Machine Learning Book on Computational Trust. Chapman and Hall/CRC Press (2014)

    Google Scholar 

  2. Eirinaki, M., Louta, M.D., Varlamis, I.: A trust-aware system for personalized user recommendations in social networks. IEEE Trans. Syst. Man Cybern. Syst. 44(4), 409–421 (2014)

    Article  Google Scholar 

  3. Yuan, W., Guan, D., Lee, S., Wang, J.: Skeleton searching strategy for recommender searching mechanism of Trust-Aware Recommender Systems. Comput. J. 58(9), 1876–1883 (2015)

    Article  Google Scholar 

  4. Riondato, M., Kornaropoulos, E.M.: Fast approximation of betweenness centrality through sampling. Data Min. Knowl. Disc. 30(2), 438–475 (2016)

    Article  MathSciNet  Google Scholar 

  5. Kósa, B., Balassi, M., Englert, P., Kiss, A.: Betweenness versus linerank. Comput. Sci. Inf. Syst. 12(1), 33–48 (2015)

    Article  Google Scholar 

  6. Nasre, M., Pontecorvi, M., Ramachandran, V.: Betweenness centrality – incremental and faster. In: Csuhaj-Varjú, E., Dietzfelbinger, M., Ésik, Z. (eds.) MFCS 2014. LNCS, vol. 8635, pp. 577–588. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44465-8_49

    Google Scholar 

  7. Bergamini, E., Meyerhenke, H.: Fully-dynamic approximation of betweenness centrality. In: Bansal, N., Finocchi, I. (eds.) ESA 2015. LNCS, vol. 9294, pp. 155–166. Springer, Heidelberg (2015). doi:10.1007/978-3-662-48350-3_14

    Chapter  Google Scholar 

  8. Yuan, W., Guan, D., Lee, Y.K., Lee, S., Hur, S.J.: Improved trust-aware recommender system using small-worldness of trust networks. Knowl.-Based Syst. 23(3), 232–238 (2010)

    Article  Google Scholar 

  9. Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)

    Article  Google Scholar 

  10. Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4), 2065–2073 (2014)

    Article  Google Scholar 

  11. Champiri, Z.D., Shahamiri, S.R., Salim, S.S.B.: A systematic review of scholar context-aware recommender systems. Expert Syst. Appl. 42(3), 1743–1758 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by Nature Science Foundation of China (Grant No. 61672284), China Postdoctoral Science Foundation (Grant No. 2016M591841). This work was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (Grant No. CAAC-ITRB-201501 and Grant No. CAAC-ITRB-201602).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Li Zhou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, L., Yuan, W., He, K., Li, C., Li, Q. (2017). Kernel Searching Strategy for Recommender Searching Mechanism. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68542-7_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68541-0

  • Online ISBN: 978-3-319-68542-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics