Topic modeling for scientific paper recommendation[go to overview]
Scientific paper recommendation is a task that aims to enhance the exploitation of Digital Libraries (DL) and helps researchers to find relevant papers from a large pool of papers. However, reliable sources to model the researcher interests must be provided to have accurate recommendations.
In my research project, I focused on the extraction of the user topical interests from papers that the user is connected with (authored or rated) and also by using the social structure of the academic network of the user (relations among researchers in the same domain).
I proposed a fully Content-Based Filtering approach for scientific paper recommendation that relies on topic modeling: the profile of a researcher is modeled by a set of topics obtained by applying Latent Dirichlet Allocation (LDA) to the papers written by the researcher. The profile built by this model is easily interpretable, and can explain the recommendation results.
In recommender systems of scientific papers, the user-item rating matrix is very sparse and users are relatively few compared with the numerous available items. To overcome the issue of data sparsity in Collaborative Filtering approaches, I proposed a Collaborative Filtering scientific paper recommendation approach which uses the topics in the researcher's rated papers to define the user profiles, thus ignoring the numeric values of ratings, and applying a community detection algorithm to group similar researchers according to their related topics instead of calculating similarities based on co-rated items.
01.02.18 - 10:15