SeEn: Sequential enriched datasets for sequence-aware recommendations

The recommendation of items based on the sequential past users' preferences has evolved in the last few years, mostly due to deep learning approaches, such as BERT4Rec. However, in scientific fields, recommender systems for recommending the next best item are not widely used. The main goal of this work is to improve the results for the recommendation of the next best item in scientific domains using sequence aware datasets and algorithms. In the first part of this work, we present the adaptation of a previous method (LIBRETTI) for creating sequential recommendation datasets for scientific fields. The results were assessed in Astronomy and Chemistry. In the second part of this work, we propose a new approach to improve the datasets, not the algorithms, to obtain better recommendations. The new hybrid approach is called sequential enrichment (SeEn), which consists of adding to a sequence of items the n most similar items after each original item. The results show that the enriched sequences obtained better results than the original ones. The Chemistry dataset improved by approximately seven percentage points and the Astronomy dataset by 16 percentage points for Hit Ratio and Normalized Discounted Cumulative Gain.