Abstract
Traditionally, recommender systems have been approached as regression models aiming to predict the score that a user would give to a particular item. In this work, we propose a recommender system that tackles the problem as a classification task instead of as a regression. The new model, Dirichlet Matrix Factorization (DirMF), provides not only a prediction but also its reliability, hence achieving a better balance between the quality and quantity of the predictions (i.e., reducing the prediction error by limiting the model’s coverage). The experimental results conducted show that the proposed model outperforms other models due to its ability to discard unreliable predictions. Compared to our previous model, which uses the same classification approach, DirMF shows a similar efficiency, outperforming the former on some of the datasets included in the experimental setup.
Highlights
In the latest years, society is becoming more and more saturated with the overwhelming amount of information available to costumers
We are able to tune the output of the Dirichlet Matrix Factorization (DirMF) model by filtering out less reliable predictions, decreasing the coverage of the model as some predictions cannot be recommended because the model does not have enough confidence to make them, but increasing the prediction accuracy at the same time
Probabilistic Matrix Factorization (PMF), GMF and Neural Collaborative Filtering (NCF) are represented in the graphic as a horizontal line since varying the threshold do not affect their predictions
Summary
Society is becoming more and more saturated with the overwhelming amount of information available to costumers For this reason, the use of algorithms for information retrieval and sorting is becoming a cornerstone in the technological solutions offered. Improving recommendation accuracy has been the main target for academic researchers [1], other objectives that are beyond improving accuracy have attracted the interest of the community in recent years [2]. Metrics such as coverage, diversity and novelty are goals of cutting-edge research since users typically prefer to be recommended items with a certain degree of novelty. Almost everyone prefers a restaurant with an average rating of 4 stars and 5000 reviews to one with just 5 reviews and an average rating of
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