Towards Joint Information Retrieval and Recommender Systems

Everyone has some information need related to work tasks, entertainment or other fields. The technological components that are used to answer them usually are Information Retrieval (IR) systems and Recommender Systems (RS). Despite these two types of systems are traditionally developed in isolation, since the nineties it was clear that there were common aspects between IR and RS. Indeed, they are both concerned with retrieving the most relevant documents or items in a collection according to a user request. Only recently some efforts have been directed towards the development of joint IR and RS systems. Nonetheless, most of the created systems focus on gaining the knowledge to carry out one of the two tasks based on the data of the other. A few relevant results really addressed the issue of joint IR and RS but they present several limitations: most of existing models are jointly optimized by aggregating data from both tasks without considering that users' intents in IR and RS sometimes may be different; current models focus on personalization without considering cold-start users; lack of appropriate, public datasets suitable for training and evaluating such models. This paper outlines the author’s PhD research objectives in designing new models and resources that allow to overcome the discussed limitations.