Towards a Modular Recommender System for Research Papers written in Albanian

TL;DR

This paper introduces a modular, intelligent search and recommendation system for Albanian research papers, utilizing cosine similarity and term importance heuristics to improve relevance in a low-resource language context.

Contribution

It presents the design of a novel recommender system tailored for Albanian scientific articles, addressing the lack of existing tools for this language.

Findings

Cosine similarity heuristics effectively recommend relevant papers.

Title and abstract are sufficient for good recommendation results.

Different importance factors have minimal impact on recommendations.

Abstract

In the recent years there has been an increase in scientific papers publications in Albania and its neighboring countries that have large communities of Albanian speaking researchers. Many of these papers are written in Albanian. It is a very time consuming task to find papers related to the researchers' work, because there is no concrete system that facilitates this process. In this paper we present the design of a modular intelligent search system for articles written in Albanian. The main part of it is the recommender module that facilitates searching by providing relevant articles to the users (in comparison with a given one). We used a cosine similarity based heuristics that differentiates the importance of term frequencies based on their location in the article. We did not notice big differences on the recommendation results when using different combinations of the importance factors of the keywords, title, abstract and body. We got similar results when using only the title and abstract in comparison with the other combinations. Because we got fairly good results in this initial approach, we believe that similar recommender systems for documents written in Albanian can be build also in contexts not related to scientific publishing.