I Recommend It: A Recommender System for Scholarship Discovery

1 Oct

The rate of production of scholarship has never been higher. And while our ability to discover relevant scholarship per unit of time has never kept pace with the production of knowledge, it has also risen sharply—most recently, due to Google scholar.

The efficiency of discovery of relevant scholarship, however, has plateaued over the last few years, even as the rate of production of scholarship has kept its steady upward climb. Part of the reason why the growth has plateaued is because current ways of doing thing cannot be made considerably more efficient very quickly. New growth in rate of discovery will need knowledge discovery systems to get more data on the user’s needs, and access to structured databases of academic production.

The easiest next step would perhaps be to build a collaborative recommendation system. In particular, think of a system that takes your .bib file, trawls a large database of citation lists, for example, JSTOR or PLoS, and produces recommendations for scholarship you may have missed. The logic of a collaborative recommender system is pretty simple: learn from articles which have cited similar scholarship as you. If we have meta data on scholarship, for instance, sub-field, actual text of an article or even the abstract, we could recommend based on the extent to which two articles cite the same kind of scholarly article. Direct elicitations (search terms are but one form) from the user can also be added to guide the recommendations. And meta characteristics, for instance, page rank of a piece of scholarship, could be used to order recommendations.