2,219 research outputs found
Context-aware Sequential Recommendation
Since sequential information plays an important role in modeling user
behaviors, various sequential recommendation methods have been proposed.
Methods based on Markov assumption are widely-used, but independently combine
several most recent components. Recently, Recurrent Neural Networks (RNN) based
methods have been successfully applied in several sequential modeling tasks.
However, for real-world applications, these methods have difficulty in modeling
the contextual information, which has been proved to be very important for
behavior modeling. In this paper, we propose a novel model, named Context-Aware
Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix
and transition matrix in conventional RNN models, CA-RNN employs adaptive
context-specific input matrices and adaptive context-specific transition
matrices. The adaptive context-specific input matrices capture external
situations where user behaviors happen, such as time, location, weather and so
on. And the adaptive context-specific transition matrices capture how lengths
of time intervals between adjacent behaviors in historical sequences affect the
transition of global sequential features. Experimental results show that the
proposed CA-RNN model yields significant improvements over state-of-the-art
sequential recommendation methods and context-aware recommendation methods on
two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.Comment: IEEE International Conference on Data Mining (ICDM) 2016, to apea
Text-Guided Molecule Generation with Diffusion Language Model
Text-guided molecule generation is a task where molecules are generated to
match specific textual descriptions. Recently, most existing SMILES-based
molecule generation methods rely on an autoregressive architecture. In this
work, we propose the Text-Guided Molecule Generation with Diffusion Language
Model (TGM-DLM), a novel approach that leverages diffusion models to address
the limitations of autoregressive methods. TGM-DLM updates token embeddings
within the SMILES string collectively and iteratively, using a two-phase
diffusion generation process. The first phase optimizes embeddings from random
noise, guided by the text description, while the second phase corrects invalid
SMILES strings to form valid molecular representations. We demonstrate that
TGM-DLM outperforms MolT5-Base, an autoregressive model, without the need for
additional data resources. Our findings underscore the remarkable effectiveness
of TGM-DLM in generating coherent and precise molecules with specific
properties, opening new avenues in drug discovery and related scientific
domains. Code will be released at: https://github.com/Deno-V/tgm-dlm.Comment: Accepted by 38th Association for the Advancement of Artificial
Intelligence, AAA
Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables
Fact checking aims to predict claim veracity by reasoning over multiple
evidence pieces. It usually involves evidence retrieval and veracity reasoning.
In this paper, we focus on the latter, reasoning over unstructured text and
structured table information. Previous works have primarily relied on
fine-tuning pretrained language models or training homogeneous-graph-based
models. Despite their effectiveness, we argue that they fail to explore the
rich semantic information underlying the evidence with different structures. To
address this, we propose a novel word-level Heterogeneous-graph-based model for
Fact Checking over unstructured and structured information, namely HeterFC. Our
approach leverages a heterogeneous evidence graph, with words as nodes and
thoughtfully designed edges representing different evidence properties. We
perform information propagation via a relational graph neural network,
facilitating interactions between claims and evidence. An attention-based
method is utilized to integrate information, combined with a language model for
generating predictions. We introduce a multitask loss function to account for
potential inaccuracies in evidence retrieval. Comprehensive experiments on the
large fact checking dataset FEVEROUS demonstrate the effectiveness of HeterFC.
Code will be released at: https://github.com/Deno-V/HeterFC.Comment: Accepted by 38th Association for the Advancement of Artificial
Intelligence, AAA
Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media
With the rapid development of social media, the wide dissemination of fake
news on social media is increasingly threatening both individuals and society.
In the dynamic landscape of social media, fake news detection aims to develop a
model trained on news reporting past events. The objective is to predict and
identify fake news about future events, which often relate to subjects entirely
different from those in the past. However, existing fake detection methods
exhibit a lack of robustness and cannot generalize to unseen events. To address
this, we introduce Future ADaptive Event-based Fake news Detection (FADE)
framework. Specifically, we train a target predictor through an adaptive
augmentation strategy and graph contrastive learning to make more robust
overall predictions. Simultaneously, we independently train an event-only
predictor to obtain biased predictions. Then we further mitigate event bias by
obtaining the final prediction by subtracting the output of the event-only
predictor from the output of the target predictor. Encouraging results from
experiments designed to emulate real-world social media conditions validate the
effectiveness of our method in comparison to existing state-of-the-art
approaches
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
Session-based recommendation nowadays plays a vital role in many websites,
which aims to predict users' actions based on anonymous sessions. There have
emerged many studies that model a session as a sequence or a graph via
investigating temporal transitions of items in a session. However, these
methods compress a session into one fixed representation vector without
considering the target items to be predicted. The fixed vector will restrict
the representation ability of the recommender model, considering the diversity
of target items and users' interests. In this paper, we propose a novel target
attentive graph neural network (TAGNN) model for session-based recommendation.
In TAGNN, target-aware attention adaptively activates different user interests
with respect to varied target items. The learned interest representation vector
varies with different target items, greatly improving the expressiveness of the
model. Moreover, TAGNN harnesses the power of graph neural networks to capture
rich item transitions in sessions. Comprehensive experiments conducted on
real-world datasets demonstrate its superiority over state-of-the-art methods.Comment: 5 pages, accepted to SIGIR 2020, authors' versio
Improving Molecular Pretraining with Complementary Featurizations
Molecular pretraining, which learns molecular representations over massive
unlabeled data, has become a prominent paradigm to solve a variety of tasks in
computational chemistry and drug discovery. Recently, prosperous progress has
been made in molecular pretraining with different molecular featurizations,
including 1D SMILES strings, 2D graphs, and 3D geometries. However, the role of
molecular featurizations with their corresponding neural architectures in
molecular pretraining remains largely unexamined. In this paper, through two
case studies -- chirality classification and aromatic ring counting -- we first
demonstrate that different featurization techniques convey chemical information
differently. In light of this observation, we propose a simple and effective
MOlecular pretraining framework with COmplementary featurizations (MOCO). MOCO
comprehensively leverages multiple featurizations that complement each other
and outperforms existing state-of-the-art models that solely relies on one or
two featurizations on a wide range of molecular property prediction tasks.Comment: 24 pages, work in progres
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