Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The fine-grained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of fine-grained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.Comment: Appears in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019

Text Classification: A Review, Empirical, and Experimental Evaluation

The explosive and widespread growth of data necessitates the use of text classification to extract crucial information from vast amounts of data. Consequently, there has been a surge of research in both classical and deep learning text classification methods. Despite the numerous methods proposed in the literature, there is still a pressing need for a comprehensive and up-to-date survey. Existing survey papers categorize algorithms for text classification into broad classes, which can lead to the misclassification of unrelated algorithms and incorrect assessments of their qualities and behaviors using the same metrics. To address these limitations, our paper introduces a novel methodological taxonomy that classifies algorithms hierarchically into fine-grained classes and specific techniques. The taxonomy includes methodology categories, methodology techniques, and methodology sub-techniques. Our study is the first survey to utilize this methodological taxonomy for classifying algorithms for text classification. Furthermore, our study also conducts empirical evaluation and experimental comparisons and rankings of different algorithms that employ the same specific sub-technique, different sub-techniques within the same technique, different techniques within the same category, and categorie

RecXplainer: Post-Hoc Attribute-Based Explanations for Recommender Systems

Recommender systems are ubiquitous in most of our interactions in the current digital world. Whether shopping for clothes, scrolling YouTube for exciting videos, or searching for restaurants in a new city, the recommender systems at the back-end power these services. Most large-scale recommender systems are huge models trained on extensive datasets and are black-boxes to both their developers and end-users. Prior research has shown that providing recommendations along with their reason enhances trust, scrutability, and persuasiveness of the recommender systems. Recent literature in explainability has been inundated with works proposing several algorithms to this end. Most of these works provide item-style explanations, i.e., `We recommend item A because you bought item B.' We propose a novel approach, RecXplainer, to generate more fine-grained explanations based on the user's preference over the attributes of the recommended items. We perform experiments using real-world datasets and demonstrate the efficacy of RecXplainer in capturing users' preferences and using them to explain recommendations. We also propose ten new evaluation metrics and compare RecXplainer to six baseline methods.Comment: Awarded the Best Student Paper at TEA Workshop at NeurIPS 2022. 13 page