Abstract
The search for unfamiliar experiences and novelty is one of the main drivers behind all human activities, equally important with harm avoidance and reward dependence. A recommender system personalizes suggestions to individuals to support and guide them in their exploration tasks. Personalization mechanisms and recommender systems limit serendipitous encounters by selectively guessing the next item to show to users and potentially leading them into so-called filter bubbles. In the ideal case, these recommendations, except of being accurate, should be also novel. However, up to now most platforms fail to provide both novel and accurate recommendations. For example, a well-known recommendation algorithm, such as matrix factorization (MF), tries to optimize only the accuracy criterion, while disregarding the novelty of recommended items. In order to counteract the filter bubble, we propose two models, denoted as popularity-based and distance-based NMF, that allow to trade-off the MF performance with respect to the criteria of novelty, while only minimally compromising on accuracy. Our experimental results demonstrate that we attain high accuracy by recommending also novel items