A Bisociated Research Paper Recommendation Model using BiSOLinkers

Benard M. Maake, Sunday O. Ojo, Keneilwe Zuva, Fredrick A. Mzee


In the current days of information overload, it is nearly impossible to obtain a form of relevant knowledge from massive information repositories without using information retrieval and filtering tools. The academic field daily receives lots of research articles, thus making it virtually impossible for researchers to trace and retrieve important articles for their research work. Unfortunately, the tools used to search, retrieve and recommend relevant research papers suggest similar articles based on the user profile characteristic, resulting in the overspecialization problem whereby recommendations are boring, similar, and uninteresting. We attempt to address this problem by recommending research papers from domains considered unrelated and unconnected. This is achieved through identifying bridging concepts that can bridge these two unrelated domains through their outlying concepts – BiSOLinkers. We modeled a bisociation framework using graph theory and text mining technologies. Machine learning algorithms were utilized to identify outliers within the dataset, and the accuracy achieved by most algorithms was between 96.30% and 99.49%, suggesting that the classifiers accurately classified and identified the outliers. We additionally utilized the Latent Dirichlet Allocation (LDA) algorithm to identify the topics bridging the two unrelated domains at their point of intersection. BisoNets were finally generated, conceptually demonstrating how the two unrelated domains were linked, necessitating cross-domain recommendations. Hence, it is established that recommender systems' overspecialization can be addressed by combining bisociation, topic modeling, and text mining approaches.


Bisociation; data mining; knowledge discovery; recommender system; serendipity; text mining; topic modeling.

Full Text:



S. Renjith, A. Sreekumar, And M. Jathavedan, "An Extensive Study on The Evolution of Context-Aware Personalized Travel Recommender Systems," Information Processing & Management, Vol. 57, P. 102078, 2020.

F. Ferrara, N. Pudota, And C. Tasso, "A Keyphrase-Based Paper Recommender System," In Digital Libraries and Archives, Ed: Springer, 2011, Pp. 14-25.

E. Landhuis, "Scientific Literature: Information Overload," Nature, Vol. 535, Pp. 457-458, 2016.

Nodus Labs. (2015). Divinatory Recommender Systems: Between Similarity and Serendipity. Available: Https://Noduslabs.Com/Research/Divinatory-Recommender-Systems-Similarity-Serendipity/

B. Cai, X. Zhu, And Y. Qin, "Parameters Optimization of Hybrid Strategy Recommendation Based on Particle Swarm Algorithm," Expert Systems with Applications, Vol. 168, P. 114388, 2021/04/15/ 2021.

L. Quijano-Sánchez, I. Cantador, M. E. Cortés-Cediel, And O. Gil, "Recommender Systems for Smart Cities," Information Systems, P. 101545, 2020.

S. Sridharan, "Introducing Serendipity in Recommender Systems Through Collaborative Methods," 2014.

F. Amato, V. Moscato, A. Picariello, And F. Piccialli, "Sos: A Multimedia Recommender System for Online Social Networks," Future Generation Computer Systems, Vol. 93, Pp. 914-923, 2019/04/01/ 2019.

B. M. Maake, S. O. Ojo, S. Ngwira, And T. Zuva, "Mplist: Context Aware Music Playlist," In Emerging Technologies and Innovative Business Practices for The Transformation of Societies (Emergitech), Ieee International Conference On, 2016, Pp. 309-316.

A. Biswal, M. D. Borah, And Z. Hussain, "Music Recommender System Using Restricted Boltzmann Machine with Implicit Feedback," 2021.

M. Schedl, P. Knees, And F. Gouyon, "New Paths in Music Recommender Systems Research," Presented at The Proceedings of The Eleventh Acm Conference on Recommender Systems, Como, Italy, 2017.

C. Bhatt, M. Cooper, And J. Zhao, "Seqsense: Video Recommendation Using Topic Sequence Mining," In International Conference on Multimedia Modeling, 2018, Pp. 252-263.

D. Abul-Fottouh, M. Y. Song, And A. Gruzd, "Examining Algorithmic Biases in Youtube’s Recommendations of Vaccine Videos," International Journal of Medical Informatics, Vol. 140, P. 104175, 2020.

J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, Et Al., "The Youtube Video Recommendation System," In Proceedings of The Fourth Acm Conference on Recommender Systems, 2010, Pp. 293-296.

T. Zuva, "Image Content in Shopping Recommender System for Mobile Users," 2012.

D. Horowitz, D. Contreras, And M. Salamó, "Eventaware: A Mobile Recommender System for Events," Pattern Recognition Letters, Vol. 105, Pp. 121-134, 2018/04/01/ 2018.

J. Beel, B. Gipp, S. Langer, And C. Breitinger, "Research-Paper Recommender Systems: A Literature Survey," International Journal on Digital Libraries, Pp. 1-34, 2015.

C. Nishioka and H. Ogata, "Research Paper Recommender System for University Students on The E-Book System," 2018.

B. M. Maake, S. O. Ojo, And T. Zuva, "Information Processing in Research Paper Recommender System Classes," In Research Data Access and Management in Modern Libraries, B. Raj Kumar and B. Paul, Eds., Ed Hershey, Pa, Usa: Igi Global, 2019, Pp. 90-118.

B. M. Maake, S. O. Ojo, And T. Zuva, "A Serendipitous Research Paper Recommender System," International Journal of Business and Management Studies, Vol. 11, Pp. 38-53, 2019.

S. Sowmiya and P. Hamsagayathri, "A Collaborative Approach for Course Recommendation System," In Advances in Smart Grid Technology, Ed: Springer, 2021, Pp. 527-536.

J. Son and S. B. Kim, "Academic Paper Recommender System Using Multilevel Simultaneous Citation Networks," Decision Support Systems, Vol. 105, Pp. 24-33, 2018.

J. Beel, S. Langer, M. Genzmehr, And A. Nürnberger, "Introducing Docear's Research Paper Recommender System," In Proceedings of the 13th Acm/Ieee-Cs Joint Conference on Digital Libraries, 2013, Pp. 459-460.

A. Marchand And P. Marx, "Automated Product Recommendations with Preference-Based Explanations," Journal of Retailing, Vol. 96, Pp. 328-343, 2020.

L. Steinert, "Beyond Similarity and Accuracy: A New Take on Automating Scientific Paper Recommendations," Phd, University of Duisburg-Essen, 2017.

Y. C. Zhang, D. Ó. Séaghdha, D. Quercia, And T. Jambor, "Auralist: Introducing Serendipity into Music Recommendation," In Proceedings of The Fifth Acm International Conference on Web Search and Data Mining, 2012, Pp. 13-22.

A. H. Afridi, "Transparency for Beyond-Accuracy Experiences: A Novel User Interface for Recommender Systems," Procedia Computer Science, Vol. 151, Pp. 335-344, 2019.

S. M. Mcnee, J. Riedl, And J. A. Konstan, "Being Accurate Is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems," In Chi'06 Extended Abstracts on Human Factors in Computing Systems, 2006, Pp. 1097-1101.

A. Koestler, "The Act of Creation," 1964.

M. R. Berthold, "Towards Bisociative Knowledge Discovery," In Bisociative Knowledge Discovery, R. B. Michael, Ed., Ed: Springer-Verlag, 2012, Pp. 1-10.

C. Pan and W. Li, "Research Paper Recommendation with Topic Analysis," In Computer Design and Applications (Iccda), 2010 International Conference On, 2010, Pp. V4-264-V4-268.

M. Amami, G. Pasi, F. Stella, And R. Faiz, "An Lda-Based Approach to Scientific Paper Recommendation," In International Conference on Applications of Natural Language to Information Systems, 2016, Pp. 200-210.

F. Ahmed and M. Fuge, "Creative Exploration Using Topic Based Bisociative Networks," Arxiv Preprint Arxiv:1801.10084, 2018.

S. Mednick, "The Associative Basis of The Creative Process," Psychological Review, Vol. 69, P. 220, 1962.

A. E. Fultz and K. M. Hmieleski, "The Art of Discovering and Exploiting Unexpected Opportunities: The Roles Of Organizational Improvisation And Serendipity In New Venture Performance," Journal Of Business Venturing, Vol. 36, P. 106121, 2021.

C.-L. Wu, "Discriminating the Measurement Attributes of The Three Versions of Chinese Remote Associates Test," Thinking Skills and Creativity, Vol. 33, P. 100586, 2019.

D. Kotkov, S. Wang, And J. Veijalainen, "A Survey of Serendipity in Recommender Systems," Knowledge-Based Systems, Vol. 111, Pp. 180-192, 2016.

G. M. Lunardi, G. M. Machado, V. Maran, And J. P. M. De Oliveira, "A Metric for Filter Bubble Measurement in Recommender Algorithms Considering the News Domain," Applied Soft Computing, Vol. 97, P. 106771, 2020.

B. Rawat, J. K. Samriya, N. Pandey, And S. C. Wariyal, "A Comprehensive Study on Recommendation Systems Their Issues and Future Research Direction In E-Learning Domain," Materials Today: Proceedings, 2020/12/01/ 2020.

I. C. Paraschiv, M. Dascalu, P. Dessus, S. Trausan-Matu, And D. S. Mcnamara, "A Paper Recommendation System with Readerbench: The Graphical Visualization of Semantically Related Papers and Concepts," In State-Of-The-Art and Future Directions of Smart Learning, Ed: Springer, 2016, Pp. 445-451.

S. L. Tomassen, "Research on Ontology-Driven Information Retrieval," In Otm Confederated International Conferences" On the Move to Meaningful Internet Systems", 2006, Pp. 1460-1468.

T. Kötter, K. Thiel, And M. R. Berthold, Domain Bridging Associations Support Creativity, 2010.

B. M. Maake, S. O. Ojo, And T. Zuva, "Towards A Serendipitous Research Paper Recommender System Using Bisociative Information Networks (Bisonets)," In 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (Icabcd), 2018, Pp. 1-6.

D. R. Swanson, "Migraine and Magnesium: Eleven Neglected Connections," Perspectives in Biology and Medicine, Vol. 31, Pp. 526-557, 1988.

M. JurÅ¡iÄ, B. Sluban, B. Cestnik, M. GrÄar, And N. LavraÄ, "Bridging Concept Identification for Constructing Information Networks from Text Documents," In Bisociative Knowledge Discovery, Ed: Springer, 2012, Pp. 66-90.

D. M. Blei, A. Y. Ng, And M. I. Jordan, "Latent Dirichlet Allocation," Journal of Machine Learning Research, Vol. 3, Pp. 993-1022, 2003.

B. Sluban, M. JurÅ¡iÄ, B. Cestnik, And N. LavraÄ, "Exploring the Power of Outliers for Cross-Domain Literature Mining," In Bisociative Knowledge Discovery: An Introduction to Concept, Algorithms, Tools, And Applications, M. R. Berthold, Ed., Ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, Pp. 325-337.

D. Kim, D. Seo, S. Cho, And P. Kang, "Multi-Co-Training for Document Classification Using Various Document Representations: Tf–Idf, Lda, And Doc2vec," Information Sciences, Vol. 477, Pp. 15-29, 2019/03/01/ 2019.

K. Hornik and B. Grün, "Topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Vol. 40, Pp. 1-30, 2011.

Y. Chen, H. Zhang, R. Liu, Z. Ye, And J. Lin, "Experimental Explorations on Short Text Topic Mining Between Lda and Nmf Based Schemes," Knowledge-Based Systems, Vol. 163, Pp. 1-13, 2019.

T. L. Griffiths and M. Steyvers, "Finding Scientific Topics," Proceedings of The National Academy of Sciences, Vol. 101, Pp. 5228-5235, 2004.

Y. Zhang, J. Callan, And T. Minka, "Novelty and Redundancy Detection in Adaptive Filtering," In Proceedings of the 25th Annual International Acm Sigir Conference on Research and Development In Information Retrieval, 2002, Pp. 81-88.

DOI: http://dx.doi.org/10.18517/ijaseit.12.1.14163


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development