Personalization for real-world machine-learning applications usually has to incorporate user feedback. Unfortunately, user feedback often suffers from sparsity and possible inconsistencies. Here we present an algorithm that exploits feedback for learning only when it is consistent. The user provides feedback on a small subset of the data. Based on the data representation alone, our algorithm em...

Recommender systems typically require feedback from the user to learn the user’s taste. This feedback can come in two forms: explicit and implicit. Explicit feedback consists of ratings provided by the user for a number of items, while implicit feedback comes from observing user actions on items. These actions have to be interpreted by the recommender system and translated into a rating. In thi...

Online user feedback is principally used as an information source for evaluating customers’ satisfaction for a given goods, service or software application. The increasing attitude of people towards sharing comments through the social media is making online user feedback a resource containing different types of valuable information. The huge amount of available user feedback has drawn the atten...

Traditional collaborative filtering (CF) based recommender systems tend to perform poorly when the user-item interactions/ratings are highly scarce. To address this, we propose a learning framework that improves with synthetic feedback loop (CF-SFL) simulate user feedback. The proposed consists of and virtual user. is formulated as CF model, recommending items according observed preference. est...