56 research outputs found
Enhancing Topic Extraction in Recommender Systems with Entropy Regularization
In recent years, many recommender systems have utilized textual data for
topic extraction to enhance interpretability. However, our findings reveal a
noticeable deficiency in the coherence of keywords within topics, resulting in
low explainability of the model. This paper introduces a novel approach called
entropy regularization to address the issue, leading to more interpretable
topics extracted from recommender systems, while ensuring that the performance
of the primary task stays competitively strong. The effectiveness of the
strategy is validated through experiments on a variation of the probabilistic
matrix factorization model that utilizes textual data to extract item
embeddings. The experiment results show a significant improvement in topic
coherence, which is quantified by cosine similarity on word embeddings
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification
Many recent deep learning-based solutions have widely adopted the
attention-based mechanism in various tasks of the NLP discipline. However, the
inherent characteristics of deep learning models and the flexibility of the
attention mechanism increase the models' complexity, thus leading to challenges
in model explainability. In this paper, to address this challenge, we propose a
novel practical framework by utilizing a two-tier attention architecture to
decouple the complexity of explanation and the decision-making process. We
apply it in the context of a news article classification task. The experiments
on two large-scaled news corpora demonstrate that the proposed model can
achieve competitive performance with many state-of-the-art alternatives and
illustrate its appropriateness from an explainability perspective.Comment: Findings of ACL202
Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation
Understanding and manipulating deformable objects (e.g., ropes and fabrics)
is an essential yet challenging task with broad applications. Difficulties come
from complex states and dynamics, diverse configurations and high-dimensional
action space of deformable objects. Besides, the manipulation tasks usually
require multiple steps to accomplish, and greedy policies may easily lead to
local optimal states. Existing studies usually tackle this problem using
reinforcement learning or imitating expert demonstrations, with limitations in
modeling complex states or requiring hand-crafted expert policies. In this
paper, we study deformable object manipulation using dense visual affordance,
with generalization towards diverse states, and propose a novel kind of
foresightful dense affordance, which avoids local optima by estimating states'
values for long-term manipulation. We propose a framework for learning this
representation, with novel designs such as multi-stage stable learning and
efficient self-supervised data collection without experts. Experiments
demonstrate the superiority of our proposed foresightful dense affordance.
Project page: https://hyperplane-lab.github.io/DeformableAffordanc
Topic-Centric Explanations for News Recommendation
News recommender systems (NRS) have been widely applied for online news
websites to help users find relevant articles based on their interests. Recent
methods have demonstrated considerable success in terms of recommendation
performance. However, the lack of explanation for these recommendations can
lead to mistrust among users and lack of acceptance of recommendations. To
address this issue, we propose a new explainable news model to construct a
topic-aware explainable recommendation approach that can both accurately
identify relevant articles and explain why they have been recommended, using
information from associated topics. Additionally, our model incorporates two
coherence metrics applied to assess topic quality, providing measure of the
interpretability of these explanations. The results of our experiments on the
MIND dataset indicate that the proposed explainable NRS outperforms several
other baseline systems, while it is also capable of producing interpretable
topics compared to those generated by a classical LDA topic model. Furthermore,
we present a case study through a real-world example showcasing the usefulness
of our NRS for generating explanations.Comment: 20 pages, submitted to a journa
Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly
Shape assembly aims to reassemble parts (or fragments) into a complete
object, which is a common task in our daily life. Different from the semantic
part assembly (e.g., assembling a chair's semantic parts like legs into a whole
chair), geometric part assembly (e.g., assembling bowl fragments into a
complete bowl) is an emerging task in computer vision and robotics. Instead of
semantic information, this task focuses on geometric information of parts. As
the both geometric and pose space of fractured parts are exceptionally large,
shape pose disentanglement of part representations is beneficial to geometric
shape assembly. In our paper, we propose to leverage SE(3) equivariance for
such shape pose disentanglement. Moreover, while previous works in vision and
robotics only consider SE(3) equivariance for the representations of single
objects, we move a step forward and propose leveraging SE(3) equivariance for
representations considering multi-part correlations, which further boosts the
performance of the multi-part assembly. Experiments demonstrate the
significance of SE(3) equivariance and our proposed method for geometric shape
assembly. Project page: https://crtie.github.io/SE-3-part-assembly/Comment: ICCV 2023, Project page: https://crtie.github.io/SE-3-part-assembly/
, Code:
https://github.com/crtie/Leveraging-SE-3-Equivariance-for-Learning-3D-Geometric-Shape-Assembl
Fact Check: Analyzing Financial Events from Multilingual News Sources
The explosion in the sheer magnitude and complexity of financial news data in
recent years makes it increasingly challenging for investment analysts to
extract valuable insights and perform analysis. We propose FactCheck in
finance, a web-based news aggregator with deep learning models, to provide
analysts with a holistic view of important financial events from multilingual
news sources and extract events using an unsupervised clustering method. A web
interface is provided to examine the credibility of news articles using a
transformer-based fact-checker. The performance of the fact checker is
evaluated using a dataset related to merger and acquisition (M\&A) events and
is shown to outperform several strong baselines.Comment: Dem
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects
Articulated object manipulation is a fundamental yet challenging task in
robotics. Due to significant geometric and semantic variations across object
categories, previous manipulation models struggle to generalize to novel
categories. Few-shot learning is a promising solution for alleviating this
issue by allowing robots to perform a few interactions with unseen objects.
However, extant approaches often necessitate costly and inefficient test-time
interactions with each unseen instance. Recognizing this limitation, we observe
that despite their distinct shapes, different categories often share similar
local geometries essential for manipulation, such as pullable handles and
graspable edges - a factor typically underutilized in previous few-shot
learning works. To harness this commonality, we introduce 'Where2Explore', an
affordance learning framework that effectively explores novel categories with
minimal interactions on a limited number of instances. Our framework explicitly
estimates the geometric similarity across different categories, identifying
local areas that differ from shapes in the training categories for efficient
exploration while concurrently transferring affordance knowledge to similar
parts of the objects. Extensive experiments in simulated and real-world
environments demonstrate our framework's capacity for efficient few-shot
exploration and generalization
Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
Precisely recommending candidate news articles to users has always been a
core challenge for personalized news recommendation systems. Most recent works
primarily focus on using advanced natural language processing techniques to
extract semantic information from rich textual data, employing content-based
methods derived from local historical news. However, this approach lacks a
global perspective, failing to account for users' hidden motivations and
behaviors beyond semantic information. To address this challenge, we propose a
novel model called GLORY (Global-LOcal news Recommendation sYstem), which
combines global representations learned from other users with local
representations to enhance personalized recommendation systems. We accomplish
this by constructing a Global-aware Historical News Encoder, which includes a
global news graph and employs gated graph neural networks to enrich news
representations, thereby fusing historical news representations by a historical
news aggregator. Similarly, we extend this approach to a Global Candidate News
Encoder, utilizing a global entity graph and a candidate news aggregator to
enhance candidate news representation. Evaluation results on two public news
datasets demonstrate that our method outperforms existing approaches.
Furthermore, our model offers more diverse recommendations.Comment: 10 pages, Recsys 202
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