Towards a Modular Recommender System for Research Papers written in Albanian
This addresses the time-consuming search process for Albanian-speaking researchers, but it is incremental as it applies an existing method to a new language-specific dataset.
The paper tackled the problem of finding relevant research papers written in Albanian by designing a modular recommender system, achieving fairly good results with a cosine similarity heuristic that considered term frequencies based on article location.
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.