101 research outputs found
On Identifying Hashtags in Disaster Twitter Data
Tweet hashtags have the potential to improve the search for information
during disaster events. However, there is a large number of disaster-related
tweets that do not have any user-provided hashtags. Moreover, only a small
number of tweets that contain actionable hashtags are useful for disaster
response. To facilitate progress on automatic identification (or extraction) of
disaster hashtags for Twitter data, we construct a unique dataset of
disaster-related tweets annotated with hashtags useful for filtering actionable
information. Using this dataset, we further investigate Long Short Term
Memory-based models within a Multi-Task Learning framework. The best performing
model achieves an F1-score as high as 92.22%. The dataset, code, and other
resources are available on Github
STAR:Spatio-temporal taxonomy-aware tag recommendation for citizen complaints
In modern cities, complaining has become an important way for citizens to report emerging urban issues to governments for quick response. For ease of retrieval and handling, government officials usually organize citizen complaints by manually assigning tags to them, which is inefficient and cannot always guarantee the quality of assigned tags. This work attempts to solve this problem by recommending tags for citizen complaints. Although there exist many studies on tag recommendation for textual content, few of them consider two characteristics of citizen complaints, i.e., the spatio-temporal correlations and the taxonomy of candidate tags. In this paper, we propose a novel Spatio-Temporal Taxonomy-Aware Recommendation model (STAR), to recommend tags for citizen complaints by jointly incorporating spatio-temporal information of complaints and the taxonomy of candidate tags. Specifically, STAR first exploits two parallel channels to learn representations for textual and spatio-temporal information. To effectively leverage the taxonomy of tags, we design chained neural networks that gradually refine the representations and perform hierarchical recommendation under a novel taxonomy constraint. A fusion module is further proposed to adaptively integrate contributions of textual and spatio-temporal information in a tag-specific manner. We conduct extensive experiments on a real-world dataset and demonstrate that STAR significantly performs better than state-of-the-art methods. The effectiveness of key components in our model is also verified through ablation studies
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Forecasting User Interests Through Topic Tag Predictions in Online Health Communities
The increasing reliance on online communities for healthcare information by
patients and caregivers has led to the increase in the spread of
misinformation, or subjective, anecdotal and inaccurate or non-specific
recommendations, which, if acted on, could cause serious harm to the patients.
Hence, there is an urgent need to connect users with accurate and tailored
health information in a timely manner to prevent such harm. This paper proposes
an innovative approach to suggesting reliable information to participants in
online communities as they move through different stages in their disease or
treatment. We hypothesize that patients with similar histories of disease
progression or course of treatment would have similar information needs at
comparable stages. Specifically, we pose the problem of predicting topic tags
or keywords that describe the future information needs of users based on their
profiles, traces of their online interactions within the community (past posts,
replies) and the profiles and traces of online interactions of other users with
similar profiles and similar traces of past interaction with the target users.
The result is a variant of the collaborative information filtering or
recommendation system tailored to the needs of users of online health
communities. We report results of our experiments on an expert curated data set
which demonstrate the superiority of the proposed approach over the state of
the art baselines with respect to accurate and timely prediction of topic tags
(and hence information sources of interest).Comment: Healthcare Informatics and NL
- …