Skip to main content

Slope One Meets Neighbourhood: Revisiting Slope One Predictor in Collaborative Filtering

  • Conference paper
  • First Online:
Proceedings of the Sixth International Conference on Mathematics and Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1262))

  • 322 Accesses

Abstract

Collaborative filtering (CF) framework in recommendation is a very popular technique for providing personalized recommendation. Slope one predictor is a model-based CF which has received good attention from researchers and practitioners. In this paper, we revisit the slope one predictor to incorporate strong features of neighbourhood-based CF into it for providing personalized recommendation to users. Preliminary results with two real-world datasets are very promising. Proposed technique outperforms original slope one and its performance is at par with a variant of slope one introduced recently.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

References

  1. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowledge-based systems 46:109–132

    Google Scholar 

  2. Cacheda F, Carneiro V, Fernández D, Formoso V (2011) Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans Web (TWEB) 5(1):2

    Google Scholar 

  3. Das AS, Datar M, Garg A, Rajaram S (2007) Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 271–280

    Google Scholar 

  4. Ekstrand MD, Riedl JT, Konstan JA et al (2011) Collaborative filtering recommender systems. Found Trends® Hum–Comput Int 4(2):81–173

    Google Scholar 

  5. Gao M, Zhongfu W, Jiang F (2011) Userrank for item-based collaborative filtering recommendation. Inform Process Lett 111(9):440–446

    Article  MathSciNet  Google Scholar 

  6. Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon, LR, Riedl J (1997) Grouplens: applying collaborative filtering to usenet news. Commun ACM 40(3):77–87

    Google Scholar 

  7. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 426–434

    Google Scholar 

  8. Lemire D, Maclachlan A (2005) Slope one predictors for online rating-based collaborative filtering. In: Proceedings of the 2005 SIAM international conference on data mining. SIAM, pp 471–475

    Google Scholar 

  9. Menezes D, Lacerda A, Silva L, Veloso A, Ziviani N (2013) Weighted slope one predictors revisited. In: Proceedings of the 22nd international conference on World Wide Web. ACM, pp 967–972

    Google Scholar 

  10. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on computer supported cooperative work. ACM, pp 175–186

    Google Scholar 

  11. Sarwar BM, Karypis G, Konstan JA, Riedl J et al (2011) Item-based collaborative filtering recommendation algorithms. WWW 1:285–295

    Google Scholar 

  12. Takács G, Pilászy I, Németh B, Tikk D (2008) Matrix factorization and neighbor based algorithms for the netflix prize problem. In: Proceedings of the 2008 ACM conference on recommender systems. ACM, pp 267–274

    Google Scholar 

  13. Wang P, Ye HW (2009) A personalized recommendation algorithm combining slope one scheme and user based collaborative filtering. In: 2009 international conference on industrial and information systems. IEEE, pp 152–154

    Google Scholar 

  14. Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the ninth ACM international conference on web search and data mining. ACM, pp 153–162

    Google Scholar 

  15. Zhang D (2009) An item-based collaborative filtering recommendation algorithm using slope one scheme smoothing. In: 2009 second international symposium on electronic commerce and security, vol 2. IEEE, pp 215–217

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bidyut Kumar Patra .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shaw, R., Patra, B.K. (2021). Slope One Meets Neighbourhood: Revisiting Slope One Predictor in Collaborative Filtering. In: Giri, D., Buyya, R., Ponnusamy, S., De, D., Adamatzky, A., Abawajy, J.H. (eds) Proceedings of the Sixth International Conference on Mathematics and Computing. Advances in Intelligent Systems and Computing, vol 1262. Springer, Singapore. https://doi.org/10.1007/978-981-15-8061-1_18

Download citation

Publish with us

Policies and ethics