Skip to main content
SpringerLink
Log in

Multi-source based movie recommendation with ratings and the side information

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Many recommender systems are built based on the user ratings or user interaction data collected by the content or item providers. However, one data source may only provide limited information about the items. It would be helpful, if information about the candidate items could be retrieved from multiple data sources. In this work, a movie recommender system is designed relying on a variety of data sources that provide different types of user feedbacks on movies, including the MovieLens and Netflix rating data, YouTube movie trailer data, and movie-related tweets from Twitter. The feedbacks on movie trailers such as likes, comments, and tweets can be considered as the side information of the movies. They can be represented as movie features and then integrated with the movie ratings. Or, some of them (e.g., sentiments of the comments) can be represented as the implicit rating matrix and then integrated with the explicit ratings. The experiment shows that the inclusion of the trailer data improves the recommendation accuracy, and the most accurate result is achieved when all the feedback data is combined as the movie features.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Notes

  1. https://www.kaggle.com/netflix-inc/netflix-prize-data.

  2. https://grouplens.org/datasets/movielens/.

References

  • Arora A, Taneja V, Parashar S, Mishra A (2016) Cross-domain based event recommendation using tensor factorization. Open Computer Science 6(1):126–137

    Article  Google Scholar 

  • Bennett J, Lanning S (2007) The Netflix prize. In: Proceedings of KDD Cup and Workshop, pp. 35

  • Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Machine Learn Res 3:993–1022

    MATH  Google Scholar 

  • Cheng H, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, Anil R (2016) Wide & deep learning for recommender systems. In: the Proceedings of the 1st workshop on deep learning for recommender systems, pp. 7–10

  • Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: the Proceedings of the 10th ACM conference on recommender systems, pp. 191–198

  • Cremonesi P, Turrin R, Lentini E, Matteucci M(2008) An evaluation methodology for collaborative recommender systems. In: International Conference on Automated Solutions for Cross Media Content and Multi-Channel Distribution. IEEE, pp. 224–231

  • Davidson J, Liebald B, Liu J, Nandy P, Van Vleet T, Gargi U, Gupta S, He Y, Lambert M, Livingston B, Sampath D (2010) The YouTube video recommendation system. In: the Proceedings of the fourth ACM conference on Recommender systems, pp. 293–296

  • Du X, Yin H, Chen L, Wang Y, Yang Y, Zhou X (2018) Personalized video recommendation using rich contents from videos. IEEE Trans Knowl Data Eng 32(3):492–505

    Article  Google Scholar 

  • Farseev A, Nie L, Akbari M, Chua TS (2015) Harvesting multiple sources for user profile learning: a big data study. In: the Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 235–242

  • Fernández-Tobías I, Cantador I, Kaminskas M, Ricci F (2012) Cross-domain recommender systems: A survey of the state of the art. In: Spanish conference on information retrieval, pp. 1–12, sn.

  • Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction, arXiv preprint arXiv:1703.04247

  • Guy I, Zwerdling N, Ronen I, Carmel D, Uziel E (2010) Social media recommendation based on people and tags. In: the Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 194–201

  • Harper FM, Konstan JA (2015) The movielens datasets History and context. Acm Transactions on Interactive Intelligent Systems 5(4):1–19

    Article  Google Scholar 

  • He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: the Proceedings of the 26th international conference on world wide web, pp. 173–182

  • Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22(1):5–53

    Article  Google Scholar 

  • Hutto CJ, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on weblogs and social media IMDb, [Online]. Available: https://datasets.imdbws.com/. [Accessed 1 12 2019]

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

    Article  Google Scholar 

  • Li F, Xu G, Cao L (2016) Two-level matrix factorization for recommender systems. Neural Comput Appl 27(8):2267–2278

    Article  Google Scholar 

  • Lian J, Zhang F, Xie X, Sun G (2017) CCCFNet: A content-boosted collaborative filtering neural network for cross-domain recommender systems. In: the Proceedings of the 26th international conference on World Wide Web companion, pp. 817–818

  • Lops P, De Gemmis M, Semeraro G (2011) Recommender systems handbook. Springer, Berlin

    Google Scholar 

  • Mei T, Yang B, Hua XS, Li S (2011) Contextual video recommendation by multimodal relevance and user feedback. ACM Trans Info Sys (TOIS) 29(2):1–24

    Article  Google Scholar 

  • Núñez-Valdez ER, Quintana D, Crespo RG, Isasi P, Herrera-Viedma E (2018) A recommender system based on implicit feedback for selective dissemination of ebooks. Inf Sci 467:87–98

    Article  Google Scholar 

  • Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: the Proceedings of the 24th international conference on Machine learning, pp. 791–798

  • Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: the Proceedings of the 10th international conference on World Wide Web, pp. 285–295

  • Shi C, Hu B, Zhao X, Yu P (2018) Heterogeneous Information Network Embedding for Recommendation. IEEE Trans Knowl Data Eng 31(2):357–370

    Article  Google Scholar 

  • Shi Y, Larson M, Hanjalic A (2010) Mining mood-specific movie similarity with matrix factorization for context-aware recommendation. In: Proceedings of the workshop on context-aware movie recommendation, pp.34–40

  • Sun Z, Han L, Huang W, Wang X, Zeng X, Wang M, Yan H (2015) Recommender systems based on social networks. J Syst Softw 99:109–119

    Article  Google Scholar 

  • Taneja A, Arora A (2018) Cross domain recommendation using multidimensional tensor factorization. Expert Syst Appl 92:304–316

    Article  Google Scholar 

  • Taneja A, Arora A (2019) Modeling user preferences using neural networks and tensor factorization model. Int J Inf Manage 45:132–148

    Article  Google Scholar 

  • Tang J, Gao H, Hu X, Liu H (2013) Exploiting homophily effect for trust prediction. In: Proceedings of the sixth ACM international conference on Web search and data mining, pp. 53–62

  • Yan H, Yang C, Yu D, Li Y, Jin D, Chiu DM (2019) Multi-site user behavior modeling and its application in video recommendation. IEEE Trans Knowl Data Eng 33(1):180–193

    Article  Google Scholar 

  • Yan M, Sang J, Xu C (2015) Unified YouTube video recommendation via cross-network collaboration. In: the Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 19–26

  • Yang B, Mei T, Hua XS, Yang L, Yang SQ, Li M (2007) Online video recommendation based on multimodal fusion and relevance feedback. In: the Proceedings of the 6th ACM international conference on Image and video retrieval, pp. 73–80

  • Zhao YL, Nie L, Wang X, Chua TS (2014) Personalized recommendations of locally interesting venues to tourists via cross-region community matching. ACM Trans Intell Sys Tech (TIST) 5(3):1–26

    Article  Google Scholar 

  • Zhu J, Zhang J, He L, Wu Q, Zhou B, Zhang C, Yu PS (2017) Broad Learning based Multi-Source Collaborative Recommendation. In: the Proceedings of the Conference on Information and Knowledge Management. ACM, pp. 1409–1418

Download references

Acknowledgements

This work is partially sponsored by the Natural Science and Engineering Research Council of Canada, Grant No 2020-04760.

Author information

Authors and Affiliations

  1. Department of Computer Science, Ryerson University, Toronto, Canada

    Debashish Roy & Chen Ding

Authors
  1. Debashish Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Debashish Roy.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roy, D., Ding, C. Multi-source based movie recommendation with ratings and the side information. Soc. Netw. Anal. Min. 11, 76 (2021). https://doi.org/10.1007/s13278-021-00785-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-021-00785-5

Keywords

Search

Navigation