83 research outputs found
Leveraging Social Foci for Information Seeking in Social Media
The rise of social media provides a great opportunity for people to reach out
to their social connections to satisfy their information needs. However,
generic social media platforms are not explicitly designed to assist
information seeking of users. In this paper, we propose a novel framework to
identify the social connections of a user able to satisfy his information
needs. The information need of a social media user is subjective and personal,
and we investigate the utility of his social context to identify people able to
satisfy it. We present questions users post on Twitter as instances of
information seeking activities in social media. We infer soft community
memberships of the asker and his social connections by integrating network and
content information. Drawing concepts from the social foci theory, we identify
answerers who share communities with the asker w.r.t. the question. Our
experiments demonstrate that the framework is effective in identifying
answerers to social media questions.Comment: AAAI 201
Whole-Chain Recommendations
With the recent prevalence of Reinforcement Learning (RL), there have been
tremendous interests in developing RL-based recommender systems. In practical
recommendation sessions, users will sequentially access multiple scenarios,
such as the entrance pages and the item detail pages, and each scenario has its
specific characteristics. However, the majority of existing RL-based
recommender systems focus on optimizing one strategy for all scenarios or
separately optimizing each strategy, which could lead to sub-optimal overall
performance. In this paper, we study the recommendation problem with multiple
(consecutive) scenarios, i.e., whole-chain recommendations. We propose a
multi-agent RL-based approach (DeepChain), which can capture the sequential
correlation among different scenarios and jointly optimize multiple
recommendation strategies. To be specific, all recommender agents (RAs) share
the same memory of users' historical behaviors, and they work collaboratively
to maximize the overall reward of a session. Note that optimizing multiple
recommendation strategies jointly faces two challenges in the existing
model-free RL model - (i) it requires huge amounts of user behavior data, and
(ii) the distribution of reward (users' feedback) are extremely unbalanced. In
this paper, we introduce model-based RL techniques to reduce the training data
requirement and execute more accurate strategy updates. The experimental
results based on a real e-commerce platform demonstrate the effectiveness of
the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge
Managemen
Attributed Network Embedding for Learning in a Dynamic Environment
Network embedding leverages the node proximity manifested to learn a
low-dimensional node vector representation for each node in the network. The
learned embeddings could advance various learning tasks such as node
classification, network clustering, and link prediction. Most, if not all, of
the existing works, are overwhelmingly performed in the context of plain and
static networks. Nonetheless, in reality, network structure often evolves over
time with addition/deletion of links and nodes. Also, a vast majority of
real-world networks are associated with a rich set of node attributes, and
their attribute values are also naturally changing, with the emerging of new
content patterns and the fading of old content patterns. These changing
characteristics motivate us to seek an effective embedding representation to
capture network and attribute evolving patterns, which is of fundamental
importance for learning in a dynamic environment. To our best knowledge, we are
the first to tackle this problem with the following two challenges: (1) the
inherently correlated network and node attributes could be noisy and
incomplete, it necessitates a robust consensus representation to capture their
individual properties and correlations; (2) the embedding learning needs to be
performed in an online fashion to adapt to the changes accordingly. In this
paper, we tackle this problem by proposing a novel dynamic attributed network
embedding framework - DANE. In particular, DANE first provides an offline
method for a consensus embedding and then leverages matrix perturbation theory
to maintain the freshness of the end embedding results in an online manner. We
perform extensive experiments on both synthetic and real attributed networks to
corroborate the effectiveness and efficiency of the proposed framework.Comment: 10 page
Development of a posture detector using a flex sensor
In this digital age, many people spend hours every day looking at their cell phones and computers. Overuse of these devices can result in users\u27 posture deterioration. Poor postures not only detract from a person\u27s appearance but can also lead to chronic back discomfort and inflammation. Even though everyone knows the detrimental effects of poor postures, it is difficult for them to correct their bad habits. This is because poor postures are gradually developed, and they are usually related to a person\u27s lifestyle. The purpose of this project was to embed a poor posture detector (a posture coach) into clothing so that users could wear it unobtrusively and comfortably. Its functions were to detect and alert users of poor postures, to help the users maintain proper postures continuously, and to help users correct their habits related to bad postures
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
Recommender systems play a crucial role in mitigating the problem of
information overload by suggesting users' personalized items or services. The
vast majority of traditional recommender systems consider the recommendation
procedure as a static process and make recommendations following a fixed
strategy. In this paper, we propose a novel recommender system with the
capability of continuously improving its strategies during the interactions
with users. We model the sequential interactions between users and a
recommender system as a Markov Decision Process (MDP) and leverage
Reinforcement Learning (RL) to automatically learn the optimal strategies via
recommending trial-and-error items and receiving reinforcements of these items
from users' feedback. Users' feedback can be positive and negative and both
types of feedback have great potentials to boost recommendations. However, the
number of negative feedback is much larger than that of positive one; thus
incorporating them simultaneously is challenging since positive feedback could
be buried by negative one. In this paper, we develop a novel approach to
incorporate them into the proposed deep recommender system (DEERS) framework.
The experimental results based on real-world e-commerce data demonstrate the
effectiveness of the proposed framework. Further experiments have been
conducted to understand the importance of both positive and negative feedback
in recommendations.Comment: arXiv admin note: substantial text overlap with arXiv:1801.0020
Graduate Employment Prediction with Bias
The failure of landing a job for college students could cause serious social
consequences such as drunkenness and suicide. In addition to academic
performance, unconscious biases can become one key obstacle for hunting jobs
for graduating students. Thus, it is necessary to understand these unconscious
biases so that we can help these students at an early stage with more
personalized intervention. In this paper, we develop a framework, i.e., MAYA
(Multi-mAjor emploYment stAtus) to predict students' employment status while
considering biases. The framework consists of four major components. Firstly,
we solve the heterogeneity of student courses by embedding academic performance
into a unified space. Then, we apply a generative adversarial network (GAN) to
overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory
(LSTM) with a novel dropout mechanism to comprehensively capture sequential
information among semesters. Finally, we design a bias-based regularization to
capture the job market biases. We conduct extensive experiments on a
large-scale educational dataset and the results demonstrate the effectiveness
of our prediction framework
The Safety and Efficiency of Surgery with Colonic Stents in Left-Sided Malignant Colonic Obstruction: A Meta-Analysis
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