Recommendation: A Less Explored Killer-App of Uncertainty?

Due to the unprecedented amount of information available, it is becoming more and more important to provide personalized recommendations on data, based on past user feedbacks. However, available user feedbacks or ratings are extremely sparse, which motivates the needs for rating prediction. The most widely adopted solution has been collaborative filtering, which (1) identifies "neighboring" users with similar tastes and (2) aggregates their ratings to predict the ratings of the given user. However, while each of such aggregation involves varying levels of uncertainty, e.g., depending on the distribution of ratings aggregated, which has not been systematically considered in recommendation, though recent study suggests such consideration can boost prediction accuracy. To consider uncertainty in rating prediction, this paper reformulates the collaborative filtering problem as aggregating community ratings into multiple predicted ratings with varying levels of certainty, based on which we identify top-k results with both high confidence and rating. We empirically study the efficiency and accuracy of our proposed framework, over a classical collaborative filtering system

A Uncertainty Perspective on Qualitative Preference

Collaborative filtering has been successfully applied for predicting a person\u27s preference on an item, by aggregating community preference on the item. Typically, collaborative filtering systems are based on based on quantitative preference modeling, which requires users to express their preferences in absolute numerical ratings. However, quantitative user ratings are known to be biased and inconsistent and also significantly more burdensome to the user than the alternative qualitative preference modeling, requiring only to specify relative preferences between the item pair. More specifically, we identify three main components of collaborative filtering-- preference representation, aggregation, and similarity computation, and view each component from a qualitative perspective. From this perspective, we build a framework, which collects only qualitative feedbacks from users. Our rating-oblivious framework was empirically validated to have comparable prediction accuracies to an (impractical) upper bound accuracy obtained by collaborative filtering system using ratings

QADiver: Interactive Framework for Diagnosing QA Models

Question answering (QA) extracting answers from text to the given question in natural language, has been actively studied and existing models have shown a promise of outperforming human performance when trained and evaluated with SQuAD dataset. However, such performance may not be replicated in the actual setting, for which we need to diagnose the cause, which is non-trivial due to the complexity of model. We thus propose a web-based UI that provides how each model contributes to QA performances, by integrating visualization and analysis tools for model explanation. We expect this framework can help QA model researchers to refine and improve their models.Comment: AAAI 2019 Demonstratio