Joint Information Retrieval and Recommendation: a Reproducibility Study

Information Retrieval (IR) and Recommender Systems (RS) represent the core components in the information access scenario. These two categories of systems are traditionally developed in isolation and have a very limited interaction. However, since the nineties it was clear that there were significant connections between IR and RS and in recent times systems performing retrieval and recommendation jointly have been created. This contributed to showing that developing joint IR and RS systems allows to improve the performance of both tasks. The current state-of-the-art in the joint IR and RS field is represented by the Unified Information Access (UIA) framework. Driven by the importance of reproducibility, in this work, we discuss the reproducibility, replicability and generalizability of UIA. First, we analyse the reproducibility degree of UIA. Then, we focus on its replicability by studying its behaviour on a public dataset. Finally, we explore its generalizability by altering the data processing and training algorithms. The obtained results show that the performance of UIA and, in general, of joint IR and RS systems, may strongly depend on the dataset used for the training and evaluation and that its stability may vary depending on the task.