A SERENDIPITOUS RESEARCH PAPER RECOMMENDER SYSTEM

Abstract

In recent times, the rate at which research papers are being processed and shared all over the internet has tremendously increased leading to information overload. Tools such as academic search engines and recommender systems have lately been adopted to help the overwhelmed researchers make right decisions regarding using, downloading and managing these millions of available research paper articles. The aim of this research is to model a spontaneous research paper recommender system that recommends serendipitous research papers from two large and normally mismatched information spaces using Bisociative Information Networks (BisoNets). Set and graph theory methods were employed to model the problem, whereas text mining methodologies were used to process textual data which was used in developing nodes and links of the BisoNets graph. Nodes were constructed from weighty keywords while links between these nodes were established through weightings determined from the co-occurrence of corresponding keywords originating from both domains. Final results from our experiments ascertain the presence of latent relationships between the two habitually incompatible domains of magnesium and migraine. Word clouds indicated that there was no obvious relationship between the two domains, but statistical significance investigations on the terms indicated the presence of very strong associations that formed information networks. The strongest links in the established information networks were further exploited to show bisociations between the two habitually incompatible matrices. BisoNets were consequently constructed, exposing terms and concepts from two discordant domains that were bisociated. These terms and concepts were utilised in querying the one domain for recommendations in another domain. Hence, serendipitous recommendations were made since our bisociative knowledge discovery methodologies revealed hidden relationships between research papers from diverse domains. Finally, it was postulated that latent relationships exist between two incompatible domains, and when well exploited, it leads to the discovery of new information and knowledge that is useful to researchers in various fields, especially those engaged in multi-disciplinary research. Further research is being conducted to identify outlier linkers and connectors between domains of diverse subjects. 

Keywords

• Bisociation,Creative knowledge discovery,Information networks,Recommender systems,Serendipity

Kaynakça

1. Adamopoulos, P. (2013). Beyond rating prediction accuracy: on new perspectives in recommender systems. Paper presented at the Proceedings of the 7th ACM conference on Recommender systems.
2. André, P., Teevan, J., & Dumais, S. T. (2009). From x-rays to silly putty via Uranus: serendipity and its role in web search. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
3. Beel, J., Langer, S., Genzmehr, M., & Nürnberger, A. (2013). Introducing Docear's research paper recommender system. Paper presented at the Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries.
4. Benlamri, R., & Zhang, X. (2014). Context-aware recommender for mobile learners. Human-centric Computing and Information Sciences, 4(1), 1-34.
5. Berthold, M. R. (2012). Towards bisociative knowledge discovery. In R. B. Michael (Ed.), Bisociative Knowledge Discovery (pp. 1-10): SpringerVerlag. Bogers, T., & Van den Bosch, A. (2008). Recommending scientific articles using citeulike. Paper presented at the Proceedings of the 2008 ACM conference on Recommender systems.
6. Bollacker, K. D., Lawrence, S., & Giles, C. L. (1998). CiteSeer: An autonomous web agent for automatic retrieval and identification of interesting publications. Paper presented at the Proceedings of the second international conference on Autonomous agents.
7. Bollacker, K. D., Lawrence, S., & Giles, C. L. (2000). Discovering relevant scientific literature on the web. IEEE Intelligent Systems and their Applications, 15(2), 42-47.
8. Ferrara, F., Pudota, N., & Tasso, C. (2011). A keyphrase-based paper recommender system Digital Libraries and Archives (pp. 14-25): Springer.

9. Gipp, B., Beel, J., & Hentschel, C. (2009). Scienstein: A research paper recommender system. Paper presented at the Proceedings of the international conference on Emerging trends in computing (ICETiC’09).
10. Juršič, M., Sluban, B., Cestnik, B., Grčar, M., & Lavrač, N. (2012). Bridging concept identification for constructing information networks from text documents Bisociative Knowledge Discovery (pp. 66-90): Springer.
11. Kotkov, D., Wang, S., & Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowledge-Based Systems, 111, 180-192.
12. Kötter, T., Thiel, K., & Berthold, M. R. (2010). Domain bridging associations support creativity.
13. Lémdani, R., Polaillon, G., Bennacer, N., & Bourda, Y. (2011). A semantic similarity measure for recommender systems. Paper presented at the Proceedings of the 7th International Conference on Semantic Systems.
14. McNee, S. M., Kapoor, N., & Konstan, J. A. (2006). Don't look stupid: avoiding pitfalls when recommending research papers. Paper presented at the Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work.
15. Nascimento, C., Laender, A. H., da Silva, A. S., & Gonçalves, M. A. (2011). A source independent framework for research paper recommendation. Paper presented at the Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries.
16. Nodus Labs. (2015). Divinatory Recommender Systems: between Similarity and Serendipity. Retrieved from https://noduslabs.com/research/divinatoryrecommender-systems-similarity-serendipity/
17. Paraschiv, I. C., Dascalu, M., Dessus, P., Trausan-Matu, S., & McNamara, D. S. (2016). A Paper Recommendation System with ReaderBench: The Graphical Visualization of Semantically Related Papers and Concepts State-of-the-Art and Future Directions of Smart Learning (pp. 445-451): Springer.
18. Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems Recommender systems handbook (pp. 257-297): Springer.
19. Sridharan, S. (2014). Introducing serendipity in recommender systems through collaborative methods.
20. Swanson, D. R. (1988). Migraine and magnesium: eleven neglected connections. Perspectives in biology and medicine, 31(4), 526-557.
21. Vellino, A., & Zeber, D. (2007). A hybrid, multi-dimensional recommender for journal articles in a scientific digital library. Paper presented at the Proceedings of the 2007 IEEE/WIC/ACM international conference on web intelligence and international conference on intelligent agent technology.
22. Wang, G., He, X., & Ishuga, C. I. (2018). HAR-SI: A novel hybrid article recommendation approach integrating with social information in scientific social network. Knowledge-Based Systems, 148, 85-99. doi:https://doi.org/10.1016/j.knosys.2018.02.024
23. Zarrinkalam, F., & Kahani, M. (2012). A multi-criteria hybrid citation recommendation system based on linked data. Paper presented at the Computer and Knowledge Engineering (ICCKE), 2012 2nd International eConference on.
24. Zhao, W., Wu, R., Dai, W., & Dai, Y. (2015). Research Paper Recommendation Based on the Knowledge Gap. Paper presented at the 2015 IEEE International Conference on Data Mining Workshop (ICDMW).