Claudiu-Cristian Musat

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Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as ...

In this paper, we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about user preferences. We expand the variational autoencoder collaborative filtering from a single-domain to a multi-domain setting. T ...

Each tweet is limited to 140 characters. This constraint surprisingly makes Twitter a more spontaneous platform to express our emotions. Detecting emotions and correctly classifying them automatically is an increasingly important task if we want to underst ...

Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsampling. However, in real-world SR, there is a large variety of realistic image de ...

Automated predictions require explanations to be interpretable by humans. Past work used attention and rationale mechanisms to find words that predict the target variable of a document. Often though, they result in a tradeoff between noisy explanations or ...

The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness not ...

Emoticons are widely used to express positive or negative sentiment on Twitter. We report on a study with live users to determine whether emoticons are used to merely emphasize the sentiment of tweets, or whether they are the main elements carrying the sen ...

Multi-objective gradient methods are becoming the standard for solving multi-objective problems. Among others, they show promising results in developing multi-objective recommender systems with both correlated and conflicting objectives. Classic multi-grad ...

In this paper we create a model of human behavior in online communities, based on the network topology and on the communication content. The model contains eleven distinct hypotheses, which validate three intuitions. The rst intuition is that the network t ...

In this paper we investigate the impact of antagonism in online discussions. We define antagonism as a new class of textual opinions - direct sentiment towards the authors of previous comments. We detect the negative sentiment using aspect-based opinion mi ...

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting ...

Neural Architecture Search (NAS) aims to facilitate the design of deep networks fornew tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely b ...

Simulating online recommender system performance is no- toriously difficult and the discrepancy between the online and offline behaviors is typically not accounted for in offline evaluations. Recommender systems research tends to evalu- ate model performan ...

Claudiu-Cristian Musat

Chattez avec Graph Search

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts

Semi-Supervised Method for Multi-Category Emotion Recognition in Tweets

Benefiting From Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

Multi-Dimensional Explanation of Target Variables from Documents

Addressing fairness in classification with a model-agnostic multi-objective algorithm

A :) Is Worth a Thousand Words: How People Attach Sentiment to Emoticons and Words in Tweets

Momentum-based Gradient Methods in Multi-Objective Recommendation

A Model of Online Social Interactions based on Sentiment Analysis and Content Similarity

Direct Negative Opinions in Online Discussions

Interacting with Explanations through Critiquing

Evaluating the search phase of neural architecture search

Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context