Skip to main content
SpringerLink
Log in

Knowledge graph summarization impacts on movie recommendations

  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

A classical problem that frequently compromises Recommender System (RS) accuracy is the sparsity of the data about the interactions of the users with the items to be recommended. The use of side information (e.g. movie domain information) from a Knowledge Graph (KG) has proven effective to circumvent this problem. However, KG growth in terms of size and complexity gives rise to many challenges, including the demand for high-cost algorithms to handle large amounts of partially irrelevant and noisy data. Meanwhile, though Graph Summarization (GS) has become popular to support tasks such as KG visualization and search, it is still relatively unexplored in the KG-based RS domain. In this work, we investigate the potential of GS as a preprocessing step to condense side information in a KG and consequently reduce computational costs of using this information. We propose a GS method that combines embedding based on latent semantics (ComplEx) with nodes clustering (K-Means) in single-view and multi-view approaches for KG summarization, i.e. which act on the whole KG at once or on a separated KG view at a time, respectively. Then, we evaluate the impacts of these alternative GS approaches on several state-of-the-art KG-based RSs, in experiments using the MovieLens 1M dataset and side information gathered from IMDb and DBpedia. Our experimental results show that KG summarization can speed up the recommendation process without significant changes in movie recommendation quality, which vary in accordance with the GS approach, the summarization ratio, and the recommendation method.

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

Similar content being viewed by others

Code Availability

The code implemented during the current study are available in the kg-summ-rec repository, https://github.com/juarezsacenti/kg-summ-rec

Notes

  1. Available in: https://datasets.imdbws.com

  2. Available in: https://dbpedia.org

  3. Available in: https://github.com/sunzhuntu/Recurrent-Knowledge-Graph-Embedding

  4. Available in: https://github.com/TaoMiner/joint-kg-recommender

  5. Aval.: https://github.com/sisinflab/LODrecsys-datasets/tree/master/Movielens1M

  6. The complete list of parameters is available in github.com/juarezsacenti/kg-summ-rec

  7. Available in: https://github.com/TaoMiner/joint-kg-recommender

References

  • Aggarwal, C.C., & et al. (2016). Recommender Systems Vol. 1. Berlin: Springer.

    Book  Google Scholar 

  • Ali, S.M., & et al. (2020). Topic and sentiment aware microblog summarization for twitter. Journal of Intelligent Information System, 54(1), 129–156.

    Article  Google Scholar 

  • Arshadi, N., & Jurisica, I. (2004). Maintaining case-based reasoning systems: a machine learning approach. In Adv in case-based reason (pp. 17–31). Berlin: Springer.

  • Ayesha, S., Hanif, M.K., & Talib, R. (2020). Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion, 59, 44–58. https://doi.org/10.1016/j.inffus.2020.01.005.

    Article  Google Scholar 

  • Bickel, S., & Scheffer, T. (2004). Multi-view clustering. In ICDM, (Vol. 4 pp. 19–26).

  • Bollacker, K., & et al. (2008). Freebase: a collaboratively created graph database for structuring human knowledge. In Proc ACM SIGMOD int conf manag of data.

  • Bordes, A., & et al. (2013). Translating embeddings for modeling multi-relational data. In Adv in neural inf processing syst, pp 2787–2795.

  • Cao, Y., & et al. (2019). Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In The world wide web conf, pp 151–161.

  • Čebirić, Š, & et al. (2019). Summarizing semantic graphs: a survey. VLDB J, 28(3), 295–327. https://doi.org/10.1007/s00778-018-0528-3.

    Article  Google Scholar 

  • Costabello, L., & et al. (2019). AmpliGraph: a library for representation learning on knowledge graphs. https://doi.org/10.5281/zenodo.2595043.

  • Cunningham, P., & Delany, S.J. (2020). k-Nearest neighbour classifiers: 2nd edition (with python examples). arXiv:2004.04523.

  • Da Costa, A., & et al. (2018). Case recommender: a flexible and extensible python framework for recommender systems. In Proc 12th ACM conf recomm syst, recsys ’18, pp. 494–495, ACM, NY, USA. https://doi.org/10.1145/3240323.3241611.

  • Fernandes, B.B., Sacenti, J.A.P., & Willrich, R. (2017). Using implicit feedback for neighbors selection: Alleviating the sparsity problem in collaborative recommendation systems. In Proc 23rd braz symp multimed and the web, webmedia 2017, gramado, Brazil, pp. 341–348, ACM. https://doi.org/10.1145/3126858.3126896.

  • Fiorucci, M., Pelosin, F., & Pelillo, M. (2020). Separating structure from noise in large graphs using the regularity lemma. Pattern Recognition, 98, 107,070.

    Article  Google Scholar 

  • Garcia, S., & et al. (2012). Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3), 417–435. https://doi.org/10.1109/TPAMI.2011.142.

    Article  Google Scholar 

  • Guo, G., Zhang, J., & Yorke-Smith, N. (2015). Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowledge-Based Systems, 74, 14–27. https://doi.org/10.1016/j.knosys.2014.10.016.

    Article  Google Scholar 

  • Guo, Q., & et al. (2020). A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data 1–1. https://doi.org/10.1109/TKDE.2020.3028705.

  • Harper, F.M., & Konstan, J.A. (2015). The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TIIS), 5(4), 1–19.

    Google Scholar 

  • Hassanzadeh, O., & Consens, M.P. (2009). Linked movie data base. In LDOW.

  • Hussain, S.F., Mushtaq, M., & Halim, Z. (2014). Multi-view document clustering via ensemble method. Journal of Intelligent Information System, 43(1), 81–99.

    Article  Google Scholar 

  • Leake, D.B., & Wilson, D.C. (1998). Categorizing case-base maintenance: Dimensions and directions. In Adv in case-based reason (pp. 196–207). Berlin: Springer.

  • Lehmann, J., & et al. (2015). Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia. Semant web, 6(2), 167–195.

    Article  Google Scholar 

  • Lin, Y., & et al. (2015). Learning entity and relation embeddings for knowledge graph completion. In 9th AAAI Conf Artif Intell pp 2181–2187.

  • Liu, Q., Cheng, G., & Qu, Y. (2020). Deeplens: Deep learning for entity summarization.

  • Liu, Y., & et al. (2018). Graph summarization methods and applications: A survey. ACM Computing Surveys 51(3):1–34. https://doi.org/10.1145/3186727. article number 62.

  • Mesas, R.M., & Bellogín, A. (2020). Exploiting recommendation confidence in decision-aware recommender systems. Journal of Intelligent Information System, 54(1), 45–78.

    Article  Google Scholar 

  • Nakhjiri, N., Salamó, M., & Sànchez-marrè, M. (2020). Reputation-based maintenance in case-based reasoning. Knowledge-Based Systems 193(105283):1–11. https://doi.org/10.1016/j.knosys.2019.105283.

  • Nie, F., Cai, G., & Li, X. (2017). Multi-view clustering and semi-supervised classification with adaptive neighbours. In Proc 35th AAAI conf artif intell, pp 2408–2414.

  • Paun, I. (2020). Efficiency-effectiveness trade-offs in recommendation systems. In 14Th ACM conf recomm syst, pp 770–775. ACM, NY, USA. https://doi.org/10.1145/3383313.3411452.

  • Peluffo-Ordóñez, D.H., Lee, J.A., & Verleysen, M. (2014). Recent methods for dimensionality reduction: a brief comparative analysis. In 22Th eur symp artif neural netw, ESANN 2014, bruges, Belgium, April 23-25, 2014.

  • Piao, G., & Breslin, J.G. (2018). Transfer learning for item recommendations and knowledge graph completion in item related domains via a co-factorization model. In Eur semant web conf, pp 496–511. Springer.

  • Ragone, A., & et al. (2017). Schema-summarization in linked-data-based feature selection for recommender systems. In Proc symp appl comput, SAC ’17, pp 330–335. ACM, NY, USA. https://doi.org/10.1145/3019612.3019837.

  • Reddy, G.T., & et al. (2020). Analysis of dimensionality reduction techniques on big data. IEEE Access, 8, 54,776–54,788. https://doi.org/10.1109/ACCESS.2020.2980942.

    Article  Google Scholar 

  • Rodríguez-García, M.A., & et al. (2019). Blinddate recommender: a context-aware ontology-based dating recommendation platform. Journal of Information Science, 45(5), 573–591. https://doi.org/10.1177/0165551518806114.

    Article  Google Scholar 

  • Sacenti, J.A.P., Willrich, R., & Fileto, R. (2018). Hybrid recommender system based on multi-hierarchical ontologies. In Proc 24th braz symp multimed and the web, webmedia 2018, Salvador, Brazil, pp 149–156. ACM. https://doi.org/10.1145/3243082.3243106.

  • Shokeen, J., & Rana, C. (2020). Social recommender systems: techniques, domains, metrics, datasets and future scope. Journal of Intelligent Information System. 54:633–667. https://doi.org/10.1007/s10844-019-00578-5.

  • Smyth, B. (1998). Case-base maintenance. In Tasks and methods in applied artif intell (pp. 507–516). Berlin: Springer.

  • Smyth, B., & Keane, M.T. (1995). Remembering to forget. In Proc 14th IJCAI. Citeseer.

  • Sorzano, C.O.S., Vargas, J., & Montano, A.P. (2014). A survey of dimensionality reduction techniques.

  • Sun, Z., & et al. (2018). Recurrent knowledge graph embedding for effective recommendation. In Proc 12th ACM conf recomm syst, recsys ’18, pp 297–305, ACM, NY, USA. https://doi.org/10.1145/3240323.3240361.

  • Sydow, M., Pikuła, M., & Schenkel, R. (2013). The notion of diversity in graphical entity summarisation on semantic knowledge graphs. Journal of Intelligent Information Systems, 41(2), 109–149.

    Article  Google Scholar 

  • Trouillon, T., & et al. (2016). Complex embeddings for simple link prediction. In Proc 33rd int conf mach learn - volume 48, ICML’16, pp 2071–2080. JMLR.org.

  • Van Der Maaten, L., Postma, E., & Van den Herik, J. (2009). Dimensionality reduction: a comparative review. Journal of Machine Learning Research, 10, 66–71.

    Google Scholar 

  • Vlachos, M., & et al. (2002). Non-linear dimensionality reduction techniques for classification and visualization. In Proc 8th ACM SIGKDD int conf knowl discov and data min, KDD ’02, pp 645–651, ACM, NY, USA. https://doi.org/10.1145/775047.775143.

  • Wang, H., & et al. (2018). Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proc 27th ACM int conf inf and knowl manag, pp 417–426.

  • Wang, Q., & et al. (2017). Knowledge graph embedding: a survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2724–2743.

    Article  Google Scholar 

  • Wang, X., & et al. (2019). KGAT: Knowledge graph attention network for recommendation. In KDD, pp 950–958.

  • Wang, Z., & et al. (2014). Knowledge graph embedding by translating on hyperplanes. In AAAI, vol 14, pp 1112–1119. Citeseer.

  • Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometric Bulletin, 1(6), 80–83.

    Article  Google Scholar 

  • Wilson, D.L. (1972). Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics, SMC-2(3), 408–421.

    Article  MathSciNet  Google Scholar 

  • Wilson, D.R., & Martinez, T.R. (1997). Instance pruning techniques. In Mach learn: Proc 14th int conf ICML’97, pp. 404–411. Morgan Kaufmann.

  • Wu, J., & et al. (2015). Trust-aware media recommendation in heterogeneous social networks. World Wide Web, 18(1), 139–157. https://doi.org/10.1007/s11280-013-0243-3.

    Article  Google Scholar 

  • Xue, Z., & et al. (2015). Gomes: a group-aware multi-view fusion approach towards real-world image clustering. In 2015 IEEE Int conf multimed and expo (ICME), pp 1–6. IEEE.

  • Yang, B., & et al. (2015). Embedding entities and relations for learning and inference in knowledge bases.

  • Yang, Y., & Wang, H. (2018). Multi-view clustering: a survey. Big Data Mining and Analytics, 1(2), 83–107. https://doi.org/10.26599/BDMA.2018.9020003.

    Article  Google Scholar 

  • Yu, H., & et al. (2018). Tag recommendation method in folksonomy based on user tagging status. Journal of Intelligent Information System, 50(3), 479–500.

    Article  Google Scholar 

  • Yu, H., & et al. (2018). Web items recommendation based on multi-view clustering. In 2018 IEEE 42Nd annual comput softw and appl conf (COMPSAC), vol 01, pp 420–425. https://doi.org/10.1109/COMPSAC.2018.00064.

  • Yu, X., & et al. (2013). Recommendation in heterogeneous information networks with implicit user feedback. In Proc 7th ACM conf recomm syst, recsys ’13, pp 347–350, ACM, NY, USA. https://doi.org/10.1145/2507157.2507230.

  • Zhang, F., & et al. (2016). Collaborative knowledge base embedding for recomm systems. In Proc 22nd ACM SIGKDD int conf knowl discov and data min, pp 353–362.

  • Zhang, N., Tian, Y., & Patel, J.M. (2010). Discovery-driven graph summarization. In 2010 IEEE 26Th int conf data eng (ICDE 2010), pp 880–891. IEEE.

  • Zhang, Y., & et al. (2018). Learning over knowledge-base embeddings for recommendation. In SIGIR.

  • Zheng, X., & et al. (2018). A tourism destination recommender system using users’ sentiment and temporal dynamics. Journal of Intelligent Information System, 51(3), 557–578.

    Article  Google Scholar 

Download references

Acknowledgements

The authors also acknowledge the members of LaPeSD and the GBD laboratories from UFSC, particularly to Prof. Márcio B. Castro, Ph.D and Prof. Luís P. F. Garcia, Ph.D.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and by the Print CAPES-UFSC “Automation 4.0”.

Author information

Authors and Affiliations

  1. Graduate Program in Computer Science (PPGCC), Federal University of Santa Catarina (UFSC), Florianópolis, Santa Catarina, Brazil

    Juarez A. P. Sacenti, Renato Fileto & Roberto Willrich

Authors
  1. Juarez A. P. Sacenti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Renato Fileto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roberto Willrich
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Related work was carried out by all the authors. The implementation of the proposal and experiments was carried out by J.A.P. Sacenti. All authors drafted, revised and approved the manuscript.

Corresponding author

Correspondence to Juarez A. P. Sacenti.

Ethics declarations

Conflict of Interests

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Availability of data and material

The datasets generated during and/or analysed during the current study are available in the sacenti-jiis-2021 repository, https://github.com/juarezsacenti/sacenti-jiis-2021

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

Sacenti, J.A.P., Fileto, R. & Willrich, R. Knowledge graph summarization impacts on movie recommendations. J Intell Inf Syst 58, 43–66 (2022). https://doi.org/10.1007/s10844-021-00650-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10844-021-00650-z

Keywords

Search

Navigation