A text-based recommender system for recommending relevant news articles

Despite recent advances in the related fields and the growing popularity of AI-based tools, small businesses, and public institutions still face challenges when implementing recommendation systems to increase profits and provide personalised services. This paper provides a comprehensive overview of state-of-the-art systems and a case study of a recommender system for a small, low-budget project, battling with several constraints: the use of uncommon natural language, the use of raw, unlabelled textual data, thus using mainly techniques coming from the field of data mining and text mining rather than relying on supervised learning methods. Another constraint was the reliance on the smaller dataset and the limited resources available for the development. This led us to explore a range of content-based methods, including the utilisation of the similarity measures applied to word embeddings derived from the Word2Vec and Doc2Vec shallow neural network models, as well as the TF-–IDF method. Additionally, a topic modelling approach utilising the Latent Dirichlet Allocation was used, as well as collaborative filtering methods, such as the algorithm using the Singular Value Decomposition method, and hybrid methods integrating the fuzzy inference and fuzzy expert systems. Some of the obstacles identified were subsequently demonstrated to be too challenging for the development of accurate recommendations. However, the item-to-item similarity solutions, which were primarily content-based, yielded satisfactory results when the threshold of 75% of the average precision of recommendations assessed by users was exceeded. One such solution was the one that used the Word2Vec-based model, which had been trained on the parameters obtained from the word similarities and analogies tests. Furthermore, an overview of alternative techniques and methodologies is provided.