15 research outputs found
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets.Comment: WWW 201
Effectively Counting s-t Simple Paths in Directed Graphs
An important tool in analyzing complex social and information networks is s-t
simple path counting, which is known to be #P-complete. In this paper, we study
efficient s-t simple path counting in directed graphs. For a given pair of
vertices s and t in a directed graph, first we propose a pruning technique that
can efficiently and considerably reduce the search space. Then, we discuss how
this technique can be adjusted with exact and approximate algorithms, to
improve their efficiency. In the end, by performing extensive experiments over
several networks from different domains, we show high empirical efficiency of
our proposed technique. Our algorithm is not a competitor of existing methods,
rather, it is a friend that can be used as a fast pre-processing step, before
applying any existing algorithm
Gaps in Information Access in Social Networks
The study of influence maximization in social networks has largely ignored
disparate effects these algorithms might have on the individuals contained in
the social network. Individuals may place a high value on receiving
information, e.g. job openings or advertisements for loans. While
well-connected individuals at the center of the network are likely to receive
the information that is being distributed through the network, poorly connected
individuals are systematically less likely to receive the information,
producing a gap in access to the information between individuals. In this work,
we study how best to spread information in a social network while minimizing
this access gap. We propose to use the maximin social welfare function as an
objective function, where we maximize the minimum probability of receiving the
information under an intervention. We prove that in this setting this welfare
function constrains the access gap whereas maximizing the expected number of
nodes reached does not. We also investigate the difficulties of using the
maximin, and present hardness results and analysis for standard greedy
strategies. Finally, we investigate practical ways of optimizing for the
maximin, and give empirical evidence that a simple greedy-based strategy works
well in practice.Comment: Accepted at The Web Conference 201
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework
Since the label collecting is prohibitive and time-consuming, unsupervised
methods are preferred in applications such as fraud detection. Meanwhile, such
applications usually require modeling the intrinsic clusters in
high-dimensional data, which usually displays heterogeneous statistical
patterns as the patterns of different clusters may appear in different
dimensions. Existing methods propose to model the data clusters on selected
dimensions, yet globally omitting any dimension may damage the pattern of
certain clusters. To address the above issues, we propose a novel unsupervised
generative framework called FIRD, which utilizes adversarial distributions to
fit and disentangle the heterogeneous statistical patterns. When applying to
discrete spaces, FIRD effectively distinguishes the synchronized fraudsters
from normal users. Besides, FIRD also provides superior performance on anomaly
detection datasets compared with SOTA anomaly detection methods (over 5%
average AUC improvement). The significant experiment results on various
datasets verify that the proposed method can better model the heterogeneous
statistical patterns in high-dimensional data and benefit downstream
applications
Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning
Code summarization generates brief natural language description given a
source code snippet, while code retrieval fetches relevant source code given a
natural language query. Since both tasks aim to model the association between
natural language and programming language, recent studies have combined these
two tasks to improve their performance. However, researchers have yet been able
to effectively leverage the intrinsic connection between the two tasks as they
train these tasks in a separate or pipeline manner, which means their
performance can not be well balanced. In this paper, we propose a novel
end-to-end model for the two tasks by introducing an additional code generation
task. More specifically, we explicitly exploit the probabilistic correlation
between code summarization and code generation with dual learning, and utilize
the two encoders for code summarization and code generation to train the code
retrieval task via multi-task learning. We have carried out extensive
experiments on an existing dataset of SQL and Python, and results show that our
model can significantly improve the results of the code retrieval task over
the-state-of-art models, as well as achieve competitive performance in terms of
BLEU score for the code summarization task.Comment: Published at The Web Conference (WWW) 2020, full pape