64 research outputs found
Mining User Interests from Social Media
Social media users readily share their preferences, life events, sentiment and opinions, and implicitly signal their thoughts, feelings, and psychological behavior. This makes social media a viable source of information to accurately and effectively mine users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and exciting opportunities for further work
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Text-based games have emerged as an important test-bed for Reinforcement
Learning (RL) research, requiring RL agents to combine grounded language
understanding with sequential decision making. In this paper, we examine the
problem of infusing RL agents with commonsense knowledge. Such knowledge would
allow agents to efficiently act in the world by pruning out implausible
actions, and to perform look-ahead planning to determine how current actions
might affect future world states. We design a new text-based gaming environment
called TextWorld Commonsense (TWC) for training and evaluating RL agents with a
specific kind of commonsense knowledge about objects, their attributes, and
affordances. We also introduce several baseline RL agents which track the
sequential context and dynamically retrieve the relevant commonsense knowledge
from ConceptNet. We show that agents which incorporate commonsense knowledge in
TWC perform better, while acting more efficiently. We conduct user-studies to
estimate human performance on TWC and show that there is ample room for future
improvement
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