Skip to main content
SpringerLink
Log in

An integration method for optimizing the use of explicit and implicit feedback in recommender systems

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The recent changes in consumption patterns and the development of the Internet have increased the diversity of user feedback in the recommender system. As a result, recent studies have highlighted the complementary integration of heterogeneous feedbacks to improve the quality of recommendations. However, existing integration methods tend to be biased toward one type of feedback, which hinders proper integration, and overlook the information loss problem caused by joint training of heterogeneous feedbacks. In this work, a novel method for integrating explicit and implicit feedback (IEIF) is proposed to generate a new user preference for the personalized recommender system. The IEIF complements the data shortage by containing various types of implicit feedback, while maintaining the item ranking results derived from user-provided explicit feedback. Through extensive experiments conducted on three real-world datasets, the superior performances of the IEIF are demonstrated with improvements in precision, recall, and NDCG, with average gains of 9.92%, 8.71%, and 6.04%, respectively, over the other integration methods.

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

Similar content being viewed by others

Data availability

Douban-book: http://shichuan.org/HIN_dataset.html (or) https://github.com/7thsword/MFPR-Datasets. Dianping: http://shichuan.org/HIN_dataset.html (or) https://github.com/7thsword/MFPR-Datasets. GoodReads: https://sites.google.com/eng.ucsd.edu/ucsdbookgraph/home.

References

  • Aljunid MF, Huchaiah MD (2022) Integratecf: integrating explicit and implicit feedback based on deep learning collaborative filtering algorithm. Expert Syst Appl 207:117933

    Article  Google Scholar 

  • Bahrani P, Minaei-Bidgoli B, Parvin H, Mirzarezaee M, Keshavarz A (2023) A hybrid semantic recommender system enriched with an imputation method. Multimed Tools Appl. p 1–34

  • Bhuvaneshwari P, Rao AN, Robinson YH (2023) Top-n recommendation system using explicit feedback and outer product based residual cnn. Wirel Pers Commun 128(2):967–983

    Article  Google Scholar 

  • Breese JS, Heckerman D, Kadie C (2013) Empirical analysis of predictive algorithms for collaborative filtering. arXiv:1301.7363

  • Chen C, Ma W, Zhang M, Wang C, Liu Y, Ma S (2023) Revisiting negative sampling vs. non-sampling in implicit recommendation. ACM Trans Inf Syst 41(1):1–25

  • Chen J, Lian D, Jin B, Zheng K, Chen E (2022) Learning recommenders for implicit feedback with importance resampling. Proceedings of the ACM Web Conference 2022:1997–2005

    Google Scholar 

  • Chen S, Peng Y (2018) Matrix factorization for recommendation with explicit and implicit feedback. Knowledge-Based Syst 158:109–117

    Article  Google Scholar 

  • Coscrato V, Bridge D (2023) Estimating and evaluating the uncertainty of rating predictions and top-n recommendations in recommender systems. ACM Trans Recomm Syst 1(2):1–34

    Article  Google Scholar 

  • Feng J, Xia Z, Feng X, Peng J (2021) Rbpr: a hybrid model for the new user cold start problem in recommender systems. Knowledge-Based Syst 214:106732

    Article  Google Scholar 

  • He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web (WWW). p 173–182

  • Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining (ICDM). IEEE. p 263–272

  • Hwang WS, Li S, Kim SW, Lee K (2018) Data imputation using a trust network for recommendation via matrix factorization. Comput Sci Inf Syst 15(2):347–368

    Article  Google Scholar 

  • Jadidinejad AH, Macdonald C, Ounis I (2019) Unifying explicit and implicit feedback for rating prediction and ranking recommendation tasks. In: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR). p 149–156

  • Kiran R, Kumar P, Bhasker B (2020) Dnnrec: a novel deep learning based hybrid recommender system. Expert Syst Appl 144:113054

    Article  Google Scholar 

  • 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 (KDD). p 426–434

  • Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  • Lee J, Lee D, Lee YC, Hwang WS, Kim SW (2016) Improving the accuracy of top-n recommendation using a preference model. Inf Sci 348:290–304

    Article  Google Scholar 

  • Lin J, He M, Pan W, Ming Z (2023) Collaborative filtering with sequential implicit feedback via learning users’ preferences over item-sets. Inf Sci 621:136–155

    Article  Google Scholar 

  • Liu J, Shi C, Hu B, Liu S, Yu PS (2017) Personalized ranking recommendation via integrating multiple feedbacks. Pacific-Asia conference on Knowledge Discovery and Data Mining (PAKDD). Springer, New York, pp 131–143

    Chapter  Google Scholar 

  • Liu NN, Xiang EW, Zhao M, Yang Q (2010) Unifying explicit and implicit feedback for collaborative filtering. In: Proceedings of the 19th ACM international Conference on Information and Knowledge Management (CIKM). p 1445–1448

  • Liu SY, Chen HH, Chen CM, Tsai MF, Wang CJ (2022a) Ipr: Interaction-level preference ranking for explicit feedback. In: Proceedings of the 45th International ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR). p 1912–1916

  • Liu Y, Wu H, Rezaee K, Khosravi MR, Khalaf OI, Khan AA, Ramesh D, Qi L (2022) Interaction-enhanced and time-aware graph convolutional network for successive point-of-interest recommendation in traveling enterprises. IEEE Trans Ind Inform 19(1):635–643

    Article  Google Scholar 

  • Loni B, Pagano R, Larson M, Hanjalic A (2016) Bayesian personalized ranking with multi-channel user feedback. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys). p 361–364

  • Ma H, King I, Lyu MR (2007) Effective missing data prediction for collaborative filtering. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR). pp 39–46

  • Moon J, Jeong Y, Chae DK, Choi J, Shim H, Lee J (2023) Comix: Collaborative filtering with mixup for implicit datasets. Inf Sci 628:254–268

    Article  Google Scholar 

  • Noulapeu Ngaffo A, El Ayeb W, Choukair Z (2021) A time-aware service recommendation based on implicit trust relationships and enhanced user similarities. J Ambient Intell Humaniz Comput 12:3017–3035

    Article  Google Scholar 

  • Qi L, Liu Y, Zhang Y, Xu X, Bilal M, Song H (2022) Privacy-aware point-of-interest category recommendation in internet of things. IEEE Internet Things J 9(21):21398–21408

    Article  Google Scholar 

  • Qiu L, Zou Q (2023) Self-training on graph neural networks for recommendation with implicit feedback. Knowledge-Based Syst. p 110727

  • Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: Autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web (WWW). p 111–112

  • Sheth P, Guo R, Cheng L, Liu H, Candan KS (2023) Causal disentanglement for implicit recommendations with network information. ACM Trans Knowl Discov Data 17(7):1–18

    Article  Google Scholar 

  • Shi C, Liu J, Zhang Y, Hu B, Liu S, Yu PS (2018) Mfpr: a personalized ranking recommendation with multiple feedback. ACM Trans Soc Comput 1(2):1–22

    Article  Google Scholar 

  • Steck H (2013) Evaluation of recommendations: rating-prediction and ranking. In: Proceedings of the 7th ACM conference on Recommender systems (RecSys). p 213–220

  • Tran Q, Tran L, Hai LC, Van Linh N, Than K (2022) From implicit to explicit feedback: a deep neural network for modeling sequential behaviours and long-short term preferences of online users. Neurocomputing 479:89–105

    Article  Google Scholar 

  • Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA, Bottou L (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(12):3371–3408

    MathSciNet  Google Scholar 

  • Wan M, McAuley J (2018) Item recommendation on monotonic behavior chains. In: Proceedings of the 12th ACM conference on recommender systems (RecSys). p 86–94

  • Wei Y, Wang X, Nie L, He X, Chua TS (2020) Graph-refined convolutional network for multimedia recommendation with implicit feedback. In: Proceedings of the 28th ACM international conference on multimedia (MM). p 3541–3549

  • Wu C, Wu F, Qi T, Liu Q, Tian X, Li J, He W, Huang Y, Xie X (2022) Feedrec: news feed recommendation with various user feedbacks. Proceedings of the ACM Web Conference 2022:2088–2097

    Google Scholar 

  • Xie T, Xu Y, Chen L, Liu Y, Zheng Z (2021) Sequential recommendation on dynamic heterogeneous information network. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), IEEE. p 2105–2110

  • Zhang Q, Cao L, Zhu C, Li Z, Sun J (2018) Coupledcf: Learning explicit and implicit user-item couplings in recommendation for deep collaborative filtering. In: International Joint Conference on Artificial Intelligence (IJCAI).

  • Zhang Y, Ai Q, Chen X, Croft WB (2017) Joint representation learning for top-n recommendation with heterogeneous information sources. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM). p 1449–1458

  • Zhang Y, Zuo W, Shi Z, Adhikari BK (2023) Integrating reviews and ratings into graph neural networks for rating prediction. J Ambient Intell Humaniz Comput 14(7):8703–8723

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1C1C1012408) and in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grants funded by the Korea government (MSIT) (No.RS-2022-00155915, Artificial Intelligence Convergence Innovation Human Resources Development (Inha University), and No.2022-0-00448, Deep Total Recall: Continual Learning for Human-Like Recall of Artificial Neural Networks, 10%).

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Inha University, Incheon, 22212, South Korea

    SeungJoo Lee & Young-Duk Seo

  2. School of Computer Science, Chungbuk National University, Cheongju-si, 28644, South Korea

    Euijong Lee

Authors
  1. SeungJoo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Euijong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Young-Duk Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Young-Duk Seo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, S., Lee, E. & Seo, YD. An integration method for optimizing the use of explicit and implicit feedback in recommender systems. J Ambient Intell Human Comput 14, 16995–17008 (2023). https://doi.org/10.1007/s12652-023-04714-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-023-04714-6

Keywords

Search

Navigation