Novelty and diversity enhancement and evaluation in recommender systems and information retrieval

The development and evaluation of Information Retrieval and Recommender Systems has traditionally focused on the relevance and accuracy of retrieved documents and recommendations, respectively. However, there is an increasing realization that accuracy alone might be a sub-optimal strategy for a successful user experience. Properties such as novelty and diversity have been explored in both fields for assessing and enhancing the usefulness of search results and recommendations. In this doctoral research we study the assessment and enhancement of both properties in the confluence of Information Retrieval and Recommender Systems.