A SERENDIPITOUS RESEARCH PAPER RECOMMENDER SYSTEM

Year 2019, Volume: 11 Issue: 1, 39 - 53, 01.01.2019

Benard Magara Maake Sunday O. Ojo Tranos Zuva

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

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