1,674 research outputs found
Language in Our Time: An Empirical Analysis of Hashtags
Hashtags in online social networks have gained tremendous popularity during
the past five years. The resulting large quantity of data has provided a new
lens into modern society. Previously, researchers mainly rely on data collected
from Twitter to study either a certain type of hashtags or a certain property
of hashtags. In this paper, we perform the first large-scale empirical analysis
of hashtags shared on Instagram, the major platform for hashtag-sharing. We
study hashtags from three different dimensions including the temporal-spatial
dimension, the semantic dimension, and the social dimension. Extensive
experiments performed on three large-scale datasets with more than 7 million
hashtags in total provide a series of interesting observations. First, we show
that the temporal patterns of hashtags can be categorized into four different
clusters, and people tend to share fewer hashtags at certain places and more
hashtags at others. Second, we observe that a non-negligible proportion of
hashtags exhibit large semantic displacement. We demonstrate hashtags that are
more uniformly shared among users, as quantified by the proposed hashtag
entropy, are less prone to semantic displacement. In the end, we propose a
bipartite graph embedding model to summarize users' hashtag profiles, and rely
on these profiles to perform friendship prediction. Evaluation results show
that our approach achieves an effective prediction with AUC (area under the ROC
curve) above 0.8 which demonstrates the strong social signals possessed in
hashtags.Comment: WWW 201
Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph
The complexity of multiagent reinforcement learning (MARL) in multiagent
systems increases exponentially with respect to the agent number. This
scalability issue prevents MARL from being applied in large-scale multiagent
systems. However, one critical feature in MARL that is often neglected is that
the interactions between agents are quite sparse. Without exploiting this
sparsity structure, existing works aggregate information from all of the agents
and thus have a high sample complexity. To address this issue, we propose an
adaptive sparse attention mechanism by generalizing a sparsity-inducing
activation function. Then a sparse communication graph in MARL is learned by
graph neural networks based on this new attention mechanism. Through this
sparsity structure, the agents can communicate in an effective as well as
efficient way via only selectively attending to agents that matter the most and
thus the scale of the MARL problem is reduced with little optimality
compromised. Comparative results show that our algorithm can learn an
interpretable sparse structure and outperforms previous works by a significant
margin on applications involving a large-scale multiagent system
Neural Interactive Collaborative Filtering
In this paper, we study collaborative filtering in an interactive setting, in
which the recommender agents iterate between making recommendations and
updating the user profile based on the interactive feedback. The most
challenging problem in this scenario is how to suggest items when the user
profile has not been well established, i.e., recommend for cold-start users or
warm-start users with taste drifting. Existing approaches either rely on overly
pessimistic linear exploration strategy or adopt meta-learning based algorithms
in a full exploitation way. In this work, to quickly catch up with the user's
interests, we propose to represent the exploration policy with a neural network
and directly learn it from the feedback data. Specifically, the exploration
policy is encoded in the weights of multi-channel stacked self-attention neural
networks and trained with efficient Q-learning by maximizing users' overall
satisfaction in the recommender systems. The key insight is that the satisfied
recommendations triggered by the exploration recommendation can be viewed as
the exploration bonus (delayed reward) for its contribution on improving the
quality of the user profile. Therefore, the proposed exploration policy, to
balance between learning the user profile and making accurate recommendations,
can be directly optimized by maximizing users' long-term satisfaction with
reinforcement learning. Extensive experiments and analysis conducted on three
benchmark collaborative filtering datasets have demonstrated the advantage of
our method over state-of-the-art methods
Heterogeneous graph learning for explainable recommendation over academic networks
With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. This study aims to understand the behavior of academic job transition and hence recommend suitable institutions for PhD graduates. Specifically, we design a deep learning model to predict the career move of early-career researchers and provide suggestions. The design is built on top of scholarly/academic networks, which contains abundant information about scientific collaboration among scholars and institutions. We construct a heterogeneous scholarly network to facilitate the exploring of the behavior of career moves and the recommendation of institutions for scholars. We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation. Moreover, we propose scholar attention and meta-path attention to discover the hidden relationships between several meta-paths. With these mechanisms, HAI provides ordered recommendations with explainability. We evaluate HAI upon a real-world dataset against baseline methods. Experimental results verify the effectiveness and efficiency of our approach. © 2021 ACM
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