NDLI: Telco User Activity Level Prediction with Massive Mobile Broadband Data

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ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 8

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 7

Issue 4(Special Issue on Crowd in Intelligent Systems, Research Note/Short Paper and Regular Papers), July 2016

Introduction to the Special Issue on Crowd in Intelligent Systems

Crowdsourcing Without a Crowd: Reliable Online Species Identification Using Bayesian Models to Minimize Crowd Size

A Crowd-Powered System for Fashion Similarity Search

Rapid Low-Cost Virtual Human Bootstrapping via the Crowd

Incentives for Effort in Crowdsourcing Using the Peer Truth Serum

PPLib: Toward the Automated Generation of Crowd Computing Programs Using Process Recombination and Auto-Experimentation

Using the Crowd to Improve Search Result Ranking and the Search Experience

Crowdsourcing Empathetic Intelligence: The Case of the Annotation of EMMA Database for Emotion and Mood Recognition

Using Crowdsourcing for Scientific Analysis of Industrial Tomographic Images

CITY FEED: A Pilot System of Citizen-Sourcing for City Issue Management

Leveraging Human Computations to Improve Schematization of Spatial Relations from Imagery

A Game-Theory Approach for Effective Crowdsource-Based Relevance Assessment

Crowdsourcing Human Annotation on Web Page Structure: Infrastructure Design and Behavior-Based Quality Control

Modality-Dependent Cross-Media Retrieval

Designing Noise-Minimal Rotorcraft Approach Trajectories

STCAPLRS: A Spatial-Temporal Context-Aware Personalized Location Recommendation System

Efficient Generalized Fused Lasso and Its Applications

Multimodular Text Normalization of Dutch User-Generated Content

Robust Decentralized Low-Rank Matrix Decomposition

Telco User Activity Level Prediction with Massive Mobile Broadband Data

Coranking the Future Influence of Multiobjects in Bibliographic Network Through Mutual Reinforcement

Multitask Low-Rank Affinity Graph for Image Segmentation and Image Annotation

Issue 3(Regular Papers, Survey Papers and Special Issue on Recommender System Benchmarks), April 2016

Issue 2(Special Issue on Causal Discovery and Inference), January 2016

Issue 1, October 2015

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 6

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 5

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 4

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 3

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 2

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 1

Telco User Activity Level Prediction with Massive Mobile Broadband Data

Content Provider	ACM Digital Library
Author	Dai, Wenyuan Yang, Qiang Zeng, Jia Yuan, Mingxuan Luo, Chen
Copyright Year	2016
Abstract	Telecommunication (telco) operators aim to provide users with optimized services and bandwidth in a timely manner. The goal is to increase user experience while retaining profit. To do this, knowing the changing behavior patterns of users through their activity levels in advance can be a great help for operators to adjust their management strategies and reduce operational risk. To achieve this goal, the operators can make use of knowledge discovered from telco’s historical mobile broadband (MBB) records to predict mobile access activity level at an early stage. In this article, we report our research in a real-world telco setting involving more than one million telco users. Our novel contribution includes representing users as documents containing a collection of changing spatiotemporal “words” that express user behavior. By extracting users’ space-time access records in MBB data, we use latent Dirichlet allocation (LDA) to learn user-specific compact topic features for user activity level prediction. We propose a scalable online expectation-maximization (OEM) algorithm that can scale LDA to massive MBB data, which is significantly faster than several state-of-the-art online LDA algorithms. Using these real-world MBB data, we confirm high performance in user activity level prediction. In addition, we show that the inferred topics indicate that future activity level anomalies correlate highly with early skewed bandwidth supply and demand relations. Thus, our prediction system can also guide the telco operators to balance the telecommunication network in terms of supply-demand relations, saving deployment costs and energy of cell towers in the future.
Starting Page	1
Ending Page	30
Page Count	30
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2856057
Volume Number	7
Issue Number	4
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-02
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mobile broadband OEM algorithm Activity level prediction Big spatiotemporal data Latent Dirichlet allocation User-specific topic features
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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PLDA+: Parallel latent dirichlet allocation with data placement and pipeline processing

Supervised labeled latent Dirichlet allocation for document categorization

Multimedia News Summarization in Search

Sequential latent Dirichlet allocation

Dynamic Latent Dirichlet Allocation Tracks Evolution of Online Hate Topics

Communication-efficient algorithms for parallel latent Dirichlet allocation

Additive regularization of topic models

Fast Online EM for Big Topic Modeling

Telco User Activity Level Prediction with Massive Mobile Broadband Data

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