Modern recommender systems may output considerably different recommendations due to small perturbations in the training data. Changes in the data from a single user will alter the recommendations as well as the recommendations of other users. In applications like healthcare, housing, and finance, this sensitivity can have adverse effects on user experience. We propose a method to stabilize a given recommender system against such perturbations. This is a challenging task due to (1) the lack of a ``reference'' rank list that can be used to anchor the outputs; and (2) the computational challenges in ensuring the stability of rank lists with respect to all possible perturbations of training data. Our method, FINEST, overcomes these challenges by obtaining reference rank lists from a given recommendation model and then fine-tuning the model under simulated perturbation scenarios with rank-preserving regularization on sampled items. Our experiments on real-world datasets demonstrate that FINEST can ensure that recommender models output stable recommendations under a wide range of different perturbations without compromising next-item prediction accuracy.

If you have ever worked with machine learning algorithms, you have likely encountered the naive Bayes algorithm. This simple yet powerful classifier is widely used in a variety of fields, including natural language processing, spam filtering, and medical diagnosis, and has a number of attractive features that make it well-suited to these tasks. At its core, the naive Bayes algorithm is a probabilistic classifier that uses Bayes' theorem to predict the class label of a given sample. It does this by estimating the posterior probability of the class given the features, using the assumption that the features are independent of one another. One of the key benefits of the naive Bayes algorithm is its simplicity.

After the Storm A sublimely simple family drama from the Japanese writer-director Hirokazu Kore-eda, a filmmaker assured enough to hide his mastery in plain sight. Nothing is overemphasized, and nothing escapes his attention. Donnie Darko A haunted miasma of youthful alienation, suburban malaise, cosmic upheaval and 1980s pop-cultural infatuation, writer-director Richard Kelly's captivatingly strange 2001 debut, starring Jake Gyllenhaal, has returned to theaters just in time for our latest brush with the apocalypse. Frantz Beautifully shot in black-and-white with the occasional warm burst of color, French writer-director François Ozon's intricately layered post-World War I drama puts a feminist spin on Ernst Lubitsch's 1932 anti-war film, "Broken Lullaby." I Am Not Your Negro As directed by the gifted Raoul Peck, this documentary on James Baldwin uses the entire spectrum of movie effects, not only spoken language but also sound, music, editing and all manner of visuals, to create a cinematic essay that is powerful and painfully relevant.