35,335 research outputs found
Metric Factorization: Recommendation beyond Matrix Factorization
In the past decade, matrix factorization has been extensively researched and
has become one of the most popular techniques for personalized recommendations.
Nevertheless, the dot product adopted in matrix factorization based recommender
models does not satisfy the inequality property, which may limit their
expressiveness and lead to sub-optimal solutions. To overcome this problem, we
propose a novel recommender technique dubbed as {\em Metric Factorization}. We
assume that users and items can be placed in a low dimensional space and their
explicit closeness can be measured using Euclidean distance which satisfies the
inequality property. To demonstrate its effectiveness, we further designed two
variants of metric factorization with one for rating estimation and the other
for personalized item ranking. Extensive experiments on a number of real-world
datasets show that our approach outperforms existing state-of-the-art by a
large margin on both rating prediction and item ranking tasks.Comment: 12 page
Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning
The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve
equivalent questions that result in the same answer as the original question.
Such a system can be used to understand and answer rare and noisy
reformulations of common questions by mapping them to a set of canonical forms.
This has large-scale applications for community Question Answering (cQA) and
open-domain spoken language question answering systems. In this paper we
describe a new QPR system implemented as a Neural Information Retrieval (NIR)
system consisting of a neural network sentence encoder and an approximate
k-Nearest Neighbour index for efficient vector retrieval. We also describe our
mechanism to generate an annotated dataset for question paraphrase retrieval
experiments automatically from question-answer logs via distant supervision. We
show that the standard loss function in NIR, triplet loss, does not perform
well with noisy labels. We propose smoothed deep metric loss (SDML) and with
our experiments on two QPR datasets we show that it significantly outperforms
triplet loss in the noisy label setting
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
Graph is an important data representation which appears in a wide diversity
of real-world scenarios. Effective graph analytics provides users a deeper
understanding of what is behind the data, and thus can benefit a lot of useful
applications such as node classification, node recommendation, link prediction,
etc. However, most graph analytics methods suffer the high computation and
space cost. Graph embedding is an effective yet efficient way to solve the
graph analytics problem. It converts the graph data into a low dimensional
space in which the graph structural information and graph properties are
maximally preserved. In this survey, we conduct a comprehensive review of the
literature in graph embedding. We first introduce the formal definition of
graph embedding as well as the related concepts. After that, we propose two
taxonomies of graph embedding which correspond to what challenges exist in
different graph embedding problem settings and how the existing work address
these challenges in their solutions. Finally, we summarize the applications
that graph embedding enables and suggest four promising future research
directions in terms of computation efficiency, problem settings, techniques and
application scenarios.Comment: A 20-page comprehensive survey of graph/network embedding for over
150+ papers till year 2018. It provides systematic categorization of
problems, techniques and applications. Accepted by IEEE Transactions on
Knowledge and Data Engineering (TKDE). Comments and suggestions are welcomed
for continuously improving this surve
Green Cellular Networks: A Survey, Some Research Issues and Challenges
Energy efficiency in cellular networks is a growing concern for cellular
operators to not only maintain profitability, but also to reduce the overall
environment effects. This emerging trend of achieving energy efficiency in
cellular networks is motivating the standardization authorities and network
operators to continuously explore future technologies in order to bring
improvements in the entire network infrastructure. In this article, we present
a brief survey of methods to improve the power efficiency of cellular networks,
explore some research issues and challenges and suggest some techniques to
enable an energy efficient or "green" cellular network. Since base stations
consume a maximum portion of the total energy used in a cellular system, we
will first provide a comprehensive survey on techniques to obtain energy
savings in base stations. Next, we discuss how heterogeneous network deployment
based on micro, pico and femto-cells can be used to achieve this goal. Since
cognitive radio and cooperative relaying are undisputed future technologies in
this regard, we propose a research vision to make these technologies more
energy efficient. Lastly, we explore some broader perspectives in realizing a
"green" cellular network technologyComment: 16 pages, 5 figures, 2 table
Conquering the rating bound problem in neighborhood-based collaborative filtering: a function recovery approach
As an important tool for information filtering in the era of socialized web,
recommender systems have witnessed rapid development in the last decade. As
benefited from the better interpretability, neighborhood-based collaborative
filtering techniques, such as item-based collaborative filtering adopted by
Amazon, have gained a great success in many practical recommender systems.
However, the neighborhood-based collaborative filtering method suffers from the
rating bound problem, i.e., the rating on a target item that this method
estimates is bounded by the observed ratings of its all neighboring items.
Therefore, it cannot accurately estimate the unobserved rating on a target
item, if its ground truth rating is actually higher (lower) than the highest
(lowest) rating over all items in its neighborhood. In this paper, we address
this problem by formalizing rating estimation as a task of recovering a scalar
rating function. With a linearity assumption, we infer all the ratings by
optimizing the low-order norm, e.g., the -norm, of the second derivative
of the target scalar function, while remaining its observed ratings unchanged.
Experimental results on three real datasets, namely Douban, Goodreads and
MovieLens, demonstrate that the proposed approach can well overcome the rating
bound problem. Particularly, it can significantly improve the accuracy of
rating estimation by 37% than the conventional neighborhood-based methods.Comment: 10 pages, 4 figure
Large-scale Collaborative Filtering with Product Embeddings
The application of machine learning techniques to large-scale personalized
recommendation problems is a challenging task. Such systems must make sense of
enormous amounts of implicit feedback in order to understand user preferences
across numerous product categories. This paper presents a deep learning based
solution to this problem within the collaborative filtering with implicit
feedback framework. Our approach combines neural attention mechanisms, which
allow for context dependent weighting of past behavioral signals, with
representation learning techniques to produce models which obtain extremely
high coverage, can easily incorporate new information as it becomes available,
and are computationally efficient. Offline experiments demonstrate significant
performance improvements when compared to several alternative methods from the
literature. Results from an online setting show that the approach compares
favorably with current production techniques used to produce personalized
product recommendations.Comment: 15 pages, 5 figure
Energy Efficient Relay-Assisted Cellular Network Model using Base Station Switching
Cellular network planning strategies have tended to focus on peak traffic scenarios rather than energy efficiency. By exploiting the dynamic nature of traffic load profiles, the prospect for greener communications in cellular access networks is evolving. For example, powering down base stations (BS) and applying cell zooming can significantly reduce energy consumption, with the overriding design priority still being to uphold a minimum quality of service (QoS). Switching off cells completely can lead to both coverage holes and performance degradation in terms of increased outage probability, greater transmit power dissipation in the up and downlinks, and complex interference management, even at low traffic loads. In this paper, a cellular network model is presented where certain BS rather than being turned off, are switched to low-powered relay stations (RS) during zero-to-medium traffic periods. Neighbouring BS still retain all the baseband signal processing and transmit signals to corresponding RS via backhaul connections, under the assumption that the RS covers the whole cell. Experimental results demonstrate the efficacy of this new BS-RS Switching technique from both an energy saving and QoS perspective, in the up and downlinks
Optimal Energy Management Strategies in Wireless Data and Energy Cooperative Communications
This paper presents a new cooperative wireless communication network strategy
that incorporates energy cooperation and data cooperation. The model
establishment, design goal formulations, and algorithms for throughput
maximization of the proposed protocol are presented and illustrated using a
three-node network with two energy harvesting (EH) user nodes and a destination
node. Transmission models are established from the performance analysis for a
total of four scenarios. Based on the models, we seek to find optimal energy
management strategies by jointly optimizing time allocation for each user,
power allocations over these time intervals, and data throughputs at user nodes
so as to maximize the sum-throughput or, alternatively, the minimum throughput
of the two users in all scenarios. An accelerated Newton barrier algorithm and
an alternative algorithm based on local quadratic approximation of the
transmission models are developed to solve the aforementioned optimization
problems. Numerical experiments under practical settings provide supportive
observations to our performance analysis.Comment: 30pages, 12 figures, journa
Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective
This article provides an overview of the state-of-art results on
communication resource allocation over space, time, and frequency for emerging
cognitive radio (CR) wireless networks. Focusing on the
interference-power/interference-temperature (IT) constraint approach for CRs to
protect primary radio transmissions, many new and challenging problems
regarding the design of CR systems are formulated, and some of the
corresponding solutions are shown to be obtainable by restructuring some
classic results known for traditional (non-CR) wireless networks. It is
demonstrated that convex optimization plays an essential role in solving these
problems, in a both rigorous and efficient way. Promising research directions
on interference management for CR and other related multiuser communication
systems are discussed.Comment: to appear in IEEE Signal Processing Magazine, special issue on convex
optimization for signal processin
Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network
This paper considers a downlink cloud radio access network (C-RAN) in which
all the base-stations (BSs) are connected to a central computing cloud via
digital backhaul links with finite capacities. Each user is associated with a
user-centric cluster of BSs; the central processor shares the user's data with
the BSs in the cluster, which then cooperatively serve the user through joint
beamforming. Under this setup, this paper investigates the user scheduling, BS
clustering and beamforming design problem from a network utility maximization
perspective. Differing from previous works, this paper explicitly considers the
per-BS backhaul capacity constraints. We formulate the network utility
maximization problem for the downlink C-RAN under two different models
depending on whether the BS clustering for each user is dynamic or static over
different user scheduling time slots. In the former case, the user-centric BS
cluster is dynamically optimized for each scheduled user along with the
beamforming vector in each time-frequency slot, while in the latter case the
user-centric BS cluster is fixed for each user and we jointly optimize the user
scheduling and the beamforming vector to account for the backhaul constraints.
In both cases, the nonconvex per-BS backhaul constraints are approximated using
the reweighted l1-norm technique. This approximation allows us to reformulate
the per-BS backhaul constraints into weighted per-BS power constraints and
solve the weighted sum rate maximization problem through a generalized weighted
minimum mean square error approach. This paper shows that the proposed dynamic
clustering algorithm can achieve significant performance gain over existing
naive clustering schemes. This paper also proposes two heuristic static
clustering schemes that can already achieve a substantial portion of the gain.Comment: 14 pages, 9 figures, to appear in IEEE Access, Special Issue on
Recent Advances in Cloud Radio Access Networks, 201
- …