Skip to main content
SpringerLink
Log in

Recency augmented hybrid collaborative movie recommendation system

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

As days rolls internet information grows exponentially and the personalized recommendation system has its vibrant area of research providing relevant information to the user needs. Hybrid recommender systems combines both implicit and explicit feedbacks from user by integrating collaborative and content based recommender system. However, the real time hybrid systems are not focusing on current temporal context of the user on providing the recommendation. This paper proposed a hybrid movie recommendation system that considers recent transactions and also demographic attributes for recommending a item to the target user. The demographic attributes aids in overcoming the cold start problem. The results of the experimental study on movie lens dataset clearly indicate that the proposed system was found to be effective by considering the recent transactions with higher ratings.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of data and material

The data and material can be downloaded from Kaggle (https://www.kaggle.com/rtatman/deceptive-opinion-spam-corpus).

Code availability

Not applicable.

References

  1. Rajabpour N (2014) Application domain of recommender system : a survey. Int J Adv Stud Comput Sci Eng IJASCSE 3(2):8–14. http://www.ijascse.org/

  2. Barragáns-Martínez AB, Costa-Montenegro E, Burguillo JC, Rey-López M, Mikic-Fonte FA, Peleteiro A (2010) A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Inf Sci 180(22):4290–4311. https://doi.org/10.1016/j.ins.2010.07.024

    Article  Google Scholar 

  3. Katarya R, Verma OP (2016) Effective collaborative movie recommender system using asymmetric user similarity and matrix factorization. In: 2016 international conference on computing, communication and automation (ICCCA), pp 71–75. https://doi.org/10.1109/CCAA.2016.7813692

  4. Wang Z, Yu X, Feng N, Wang Z (2014) An improved collaborative movie recommendation system using computational intelligence. J Vis Lang Comput 25(6):667–675. https://doi.org/10.1016/j.jvlc.2014.09.011

    Article  Google Scholar 

  5. Suryawanshi S, Narnaware M (2020) Design and analysis of collaborative filtering based recommendation. Int J Eng Appl Sci Technol 5(4):223–226

  6. Wang D, Liang Y, Xu D, Feng X, Guan R (2018) A content-based recommender system for computer science publications. Knowl-Based Syst 157(February):1–9. https://doi.org/10.1016/j.knosys.2018.05.001

    Article  Google Scholar 

  7. Bagher RC, Hassanpour H, Mashayekhi H (2017) User trends modeling for a content-based recommender system. Expert Syst Appl 87:209–219. https://doi.org/10.1016/j.eswa.2017.06.020

    Article  Google Scholar 

  8. Salehi M (2013) An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product. Int J Bus Informat Syst 14(4):480–496

  9. Sunilkumar CN (2020) A review of movie recommendation system: limitations, survey and challenges. ELCVIA Electron Lett Comput Vis Image Anal 19(3):18. https://doi.org/10.5565/rev/elcvia.1232

    Article  Google Scholar 

  10. Cami BR, Hassanpour H, Mashayekhi H (2017) A content-based movie recommender system based on temporal user preferences. In 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS) (pp. 121–125). IEEE

  11. Bahl D, Kain V, Sharma A, Sharma M (2020) A novel hybrid approach towards movie recommender systems. J Stat Manag Syst. https://doi.org/10.1080/09720510.2020.1799503

    Article  Google Scholar 

  12. Ravi L, Subramaniyaswamy V, Vijayakumar V, Chen S, Karmel A, Devarajan M (2019) Hybrid location-based recommender system for mobility and travel planning. Mob Net Appl 24(4):1226–1239

  13. Wang S, Sun G, Li Y (2020) SVD++ recommendation algorithm based on backtracking. Information (Switzerland). https://doi.org/10.3390/INFO11070369

    Article  Google Scholar 

  14. Suganeshwari G, Syed Ibrahim SP, Li G (2018) Lazy collaborative filtering with dynamic neighborhoods. Inf Discov Deliv 46(2):95–109. https://doi.org/10.1108/IDD-02-2018-0007

    Article  Google Scholar 

  15. Walek B, Fojtik V (2020) A hybrid recommender system for recommending relevant movies using an expert system. Expert Syst Appl 158:113452. https://doi.org/10.1016/j.eswa.2020.113452

    Article  Google Scholar 

  16. Sundarraj RP (2017) A latent factor model based movie recommender using smartphone browsing history. In 2017 International Conference on Research and Innovation in Information Systems (ICRIIS) (pp. 1-6). IEEE

  17. Farzan R, Brusilovsky P (2006) Social navigation support in a course recommendation system. In International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (pp. 91–100). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11768012_11

  18. Reshak KA, Dhannoon BN, Sultani ZN (2020) Explicit feedback based movie recommendation system: a survey. In: AIP conference proceedings, vol 2290 (December). https://doi.org/10.1063/5.0027433

  19. Wu G, Swaminathan V, Mitra S, Kumar R (2017) Digital content recommendation system using implicit feedback data. In: Proceedings—2017 IEEE international conference on Big Data, Big Data 2017, 2018–January, pp 2766–2771. https://doi.org/10.1109/BigData.2017.8258242

  20. Choi H, Kang Y, Kang M (2017) Pet shop recommendation system based on implicit feedback. J Dig Cont Soc 18(8):1561–1566

  21. Huang H (2016) Context-aware location recommendation using geotagged photos in social media. ISPRS Inter J Geo-Informat 5(11):195. https://doi.org/10.3390/ijgi5110195

  22. Zhu Q, Wang S, Cheng B, Sun Q, Yang F, Chang RN (2018) Context-aware group recommendation for point-of interests. IEEE Access 6:12129–12144.

  23. Ananth GS, Raghuveer K, Dayananda R, Kashyap R (2020) A novel and hybrid approach of an indian demographic movie recommender system. 7:1–10. https://doi.org/10.4236/oalib.1106483

  24. Feng J, Fengs X, Zhang N, Peng J (2018) An improved collaborative filtering method based on similarity. PLoS ONE 13(9):1–18. https://doi.org/10.1371/journal.pone.0204003

    Article  Google Scholar 

  25. Jallouli M, Lajmi S, Amous I (2020) When contextual information meets recommender systems: extended SVD++ models. Int J Comput Appl. https://doi.org/10.1080/1206212X.2020.1752971

    Article  Google Scholar 

  26. Liu NN, He L, Zhao M (2013) Social temporal collaborative ranking for context aware movie recommendation. ACM Transact Intel Syst Technol (TIST) 4(1):1–26. https://doi.org/10.1145/2414425.2414440

  27. Al-Shamri MYH (2016) User profiling approaches for demographic recommender systems. Knowl-Based Syst 100:175–187. https://doi.org/10.1016/j.knosys.2016.03.006

    Article  Google Scholar 

  28. Pálovics R, Szalai P, Pap J, Frigó E, Kocsis L, Benczúr AA (2017) Location-aware online learning for top-k recommendation. Pervasive Mob Comput 38:490–504. https://doi.org/10.1016/j.pmcj.2016.06.001

    Article  Google Scholar 

  29. Reddy MM, Kanmani RS, Surendiran DB (2020) Analysis of movie recommendation systems; with and without considering the low rated movies. In: International conference on emerging trends in information technology and engineering, Ic-ETITE 2020. https://doi.org/10.1109/ic-ETITE47903.2020.453

  30. Safoury L, Salah A (2013) Exploiting user demographic attributes for solving cold-start problem in recommender system. Lecture Notes Softw Eng 1(3):303–307. https://doi.org/10.7763/LNSE.2013.V1.66

  31. Guo M, Sun J, Meng X (2015) A neighborhood-based matrix factorization technique for recommendation. Ann Data Sci 2(3):301–316. https://doi.org/10.1007/s40745-015-0056-6

    Article  Google Scholar 

  32. Han J, Yamana H (2017) Geographical diversification in POI recommendation: toward improved coverage on interested areas. In Proceedings of the Eleventh ACM Conference on Recommender Systems (pp 224–228)

  33. Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (pp. 363–372).

  34. Agarwal A, Chauhan M (2017) Similarity measures used in recommender systems: a study. Minakshi Chauhan Int J Eng Technol Sci Res IJETSR 4(6):2394–3386. http://www.ijetsr.com. http://ijetsr.com/images/short_pdf/1498555415_619-626-ieteh326_ijetsr.pdf

Download references

Funding

This work is not funded by any person/Institutions.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal, India

    R. Sujithra Alias Kanmani & B. Surendiran

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

    S. P. Syed Ibrahim

Authors
  1. R. Sujithra Alias Kanmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Surendiran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. P. Syed Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All Author’s have contributed equally to the work. All authors have read and approved the manuscript.

Corresponding author

Correspondence to R. Sujithra Alias Kanmani.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

This material is the authors' own original work, which has not been previously published elsewhere. This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sujithra Alias Kanmani, R., Surendiran, B. & Ibrahim, S.P.S. Recency augmented hybrid collaborative movie recommendation system. Int. j. inf. tecnol. 13, 1829–1836 (2021). https://doi.org/10.1007/s41870-021-00769-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-021-00769-w

Keywords

Search

Navigation