[1] J. J. Miller, “Graph database applications and concepts with Neo4j,” Proceedings of the Southern Association for Information Systems Conference, Atlanta, GA, USA, vol. 2324, p. 36, 2013.
[2] A. Virk and R. Rani, “recommendations using graphs on Neo4j,” 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 133– 138, 2018.
[3] X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Advances in Artificial Intelligence, pp. 1–9, 2009.
[4] R. Wita, K. Bubphachuen, and J. Chawachat, “Content-Based Filtering Recommen- dation in Abstract Search Using Neo4j,” ICSEC 2017 - 21st International Computer Science and Engineering Conference 2017, Proceeding, vol. 6, pp. 136–139, 2018.
[5] visualparadigm,“Themevsepicvsuserstoryvstask,”https://www.visual-paradigm. com/scrum/theme-epic-user-story-task/, 2020, accessed: 2020-12-16.
[6] R. Chen, Q. Hua, Y. S. Chang, B. Wang, L. Zhang, and X. Kong, “A survey of col- laborative filtering-based recommender systems: from traditional methods to hybrid methods based on social networks,” IEEE Access, vol. 6, pp. 64 301–64 320, 2018.
[7] Y. Shi, M. Larson, and A. Hanjalic, “Collaborative filtering beyond the user-item ma- trix: A survey of the state of the art and future challenges,” ACM Computing Surveys, ACM 2014, pp. 1–45, 2014.
[8] M. R. McLaughlin and J. L. Herlocker, “A collaborative filtering algorithm and eval- uation metric that accurately model the user experience,” Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 329–336, 2004.
[9] Z. Yang, B. Wu, K. Zheng, X. Wang, and L. Lei, “A survey of collaborative filtering- based recommender systems for mobile internet applications,” IEEE Access, pp. 1–15, 2016.
[10] Sarwar., K. Badrul, K. George, R. Joseph, and John., “Item-Based Collaborative Fil- tering Recommendation Algorithms,” International Conference on World Wide Web WWW’01, pp. 1–11, 2001.

[11] P. Mathew, B. Kuriakose, and V. Hegde, “Book Recommendation System through content based and collaborative filtering method,” Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016, pp. 47–52, 2016.
[12] M. J. Pazzani, “Framework for collaborative, content-based and demographic filter- ing,” Artificial Intelligence Review, vol. 13, no. 5, pp. 393–408, 1999.
[13] S. Zhang, W. Wang, J. Ford, F. Makedon, and J. Pearlman, “Using singular value decomposition approximation for collaborative filtering,” Proceedings - Seventh IEEE International Conference on E-Commerce Technology, CEC 2005, vol. 2005, pp. 257– 265, 2005.
[14] Y. Shoham, “Fab:Content-Based, Collaborative Filtering for Recommendation,” Communications of the ACM, vol. 40, no. 3, 1997.
[15] P. Thorat, R. M. Goudar, and S. Barve, “Survey on Collaborative Filtering, Content- based Filtering and Hybrid Recommendation System,” International Journal of Com- puter Applications, vol. 110, no. 4, pp. 31–36, 2015.
[16] M. De Gemmis, P. Lops, G. Semeraro, and P. Basile, “Integrating tags in a semantic content-based recommender,” RecSys’08: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 163–170, 2008.
[17] C. Basu, H. Hirsh, and W. Cohen, “Recommendation as classification: Using so- cial and content-based information in recommendation,” Proceedings of the National Conference on Artificial Intelligence, pp. 714–720, 1998.
[18] M.A.RodriguezandP.Neubauer,“Constructionsfromdotsandlines,”Bulletinofthe American Society for Information Science and Technology, vol. 36, no. 6, pp. 35–41, 2010.
[19] D. Q. G. L. Udsk, Y. Duan, and Z. Lin, “Specifying architecture of knowledge graph with data graph, information graph, knowledge graph and wisdom graph,” 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), pp. 327–332, 2017.
[20] G. Bellinger, D. Castro, and A. Mills, “Data, information, knowledge, and wisdom,” 2004.
[21] W. Shalaby, B. E. Alaila, M. Korayem, L. Pournajaf, K. Aljadda, S. Quinn, and W. Zadrozny, “Help Me Find a Job: A Graph-based Approach for Job Recommenda- tion at Scale,” arXiv, pp. 1544–1553, 2017.
[22] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender sys- tems: A survey of the state-of-the-art and possible extensions,” IEEE transactions on knowledge and data engineering, vol. 17, no. 6, pp. 734–749, 2005.

[23] H. Yildirim and M. S. Krishnamoorthy, “A random walk method for alleviating the sparsity problem in collaborative filtering,” in Proceedings of the 2008 ACM confer- ence on Recommender systems, 2008, pp. 131–138.
[24] H. Chen, X. Li, and Z. Huang, “Link prediction approach to collaborative filter- ing,” in Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’05). IEEE, 2005, pp. 141–142.
[25] T. Zhou, J. Ren, M. Medo, and Y.-C. Zhang, “Bipartite network projection and per- sonal recommendation,” Physical review E, vol. 76, no. 4, p. 046115, 2007.
[26] C. C. Aggarwal, J. L. Wolf, K.-L. Wu, and P. S. Yu, “Horting hatches an egg: A new graph-theoretic approach to collaborative filtering,” in Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, 1999, pp. 201–212.
[27] W.Li,Z.Ye,M.Xin,andQ.Jin,“Socialrecommendationbasedontrustandinfluence in sns environments,” Multimedia Tools and Applications, vol. 76, no. 9, pp. 11 585– 11 602, 2017.
[28] S. Ray and A. Mahanti, “Improving prediction accuracy in trust-aware recommender systems,” in 2010 43rd Hawaii international conference on system sciences. IEEE, 2010, pp. 1–9.
[29] S. Roy and B. Ravindran, “Measuring network centrality using hypergraphs,” in Pro- ceedings of the Second ACM IKDD Conference on Data Sciences, 2015, pp. 59–68.
[30] M. Cha, H. Haddadi, F. Benevenuto, and K. Gummadi, “Measuring user influence in twitter: The million follower fallacy,” in Proceedings of the International AAAI Conference on Web and Social Media, vol. 4, 2010.
[31] G. Bathla, H. Aggarwal, and R. Rani, “A graph-based model to improve social trust and influence for social recommendation,” The Journal of Supercomputing, vol. 76, no. 6, pp. 4057–4075, 2020.
[32] B. Stark, C. Knahl, M. Aydin, M. Samarah, and K. O. Elish, “BetterChoice: A mi- graine drug recommendation system based on Neo4J,” 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017, vol. 2017- Janua, pp. 382–386, 2017.
[33] L. Lü, M. Medo, C. H. Yeung, Y.-C. Zhang, Z.-K. Zhang, and T. Zhou, “Recom- mender systems,” Physics reports, vol. 519, no. 1, pp. 1–49, 2012.
[34] R. Catherine and W. Cohen, “Personalized recommendations using knowledge graphs: A probabilistic logic programming approach,” RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, pp. 325–332, 2016.

[35] Dandelion, “The entity extraction api reference,” https://dandelion.eu/docs, 2020, ac- cessed: 2020-12-21.
[36] spacy, “spacy 101: Everything you need to know,” https://spacy.io/usage/spacy-101, 2020, accessed: 2020-12-21.
[37] P. Jaccard, “Jaccard1902(method),′′ Bulletin de la Murithienne, vol.XXXV II, pp.81− −92., 1982.
[38] V. Verma and R. K. Aggarwal, “A comparative analysis of similarity measures akin to the Jaccard index in collaborative recommendations: empirical and theoretical perspective,” Social Network Analysis and Mining, vol. 10, no. 1, pp. 1–16, 2020. [Online]. Available: https://doi.org/10.1007/s13278-020-00660-9
[39] H.Liu,Z.Hu,A.Mian,H.Tian,andX.Zhu,“Anewusersimilaritymodeltoimprove the accuracy of collaborative filtering,” Knowledge-Based Systems, vol. 56, pp. 156–166, 2014. [Online]. Available: http://dx.doi.org/10.1016/j.knosys.2013.11.006
[40] “Understanding cosine similarity and its application,” https://towardsdatascience.com/understanding-cosine-similarity-and-its-application- fd42f585296a.