Novelty-Aware Matrix Factorization Based on Items’ Popularity
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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 help them in their exploration tasks. 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 disregards the novelty of recommended items. In this paper, we propose a new model, denoted as popularity-based NMF, that allows 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.