2016 INFORMS Annual Meeting Program

210 / 561

INFORMS Nashville – 2016
210
MD06
102A-MCC
Business Analytics and Text Mining
Sponsored: Data Mining
Sponsored Session
Chair: Xiao Liu, University of Arizona, 1130 E. Helen St., Tucson, AZ,
85721, United States,
1 - A Network-based Inference Model For Estimating
Missing Attributes
Da Xu, University of Utah,
The attribute information, which is critical for recommendations, search engines,
and advertising targeting, is valuable for business intelligence. While, all the
aforementioned machine learning based applications are only capable of optimal
performance when the data utilized are of high quality. And in many cases, the
attribute information is incomplete, which makes a big obstacle in targeting
products, businesses, or promotions effectively. In this paper, we utilized both
networks and content information to infer missing business attributes, which
could benefit business recommendations, help validate online business
information, and provide better personalized offerings.
2 - Extracting Signals From Social Media Text With Natural Language
Processing, Machine Learning And Domain Adaptation
Wenli Zhang, University of Arizona, Tucson, AZ, United States,
, Sudha Ram
There has been increasing interest in using social media data for predictive
analytics in different domains. Although significant promise has been shown,
mounting evidence suggests many of the results can be misrepresented because of
the loosely structured text and noise caused by media spikes and use of
misleading phases. We introduce efficient techniques combining Natural
Language Processing and Machine Learning to extract signal from social media
text. Sophisticated domain adaptation method is introduced to address multi-
domain adaptation problem. The methodology can be used for extracting signals
in health care and other domains with a view to enabling improved predictions.
3 - The Effect Of Rating System Design On Emotion Sharing
Ying Liu, Arizona State University,
How do ratings and reviews reflect consumers’ overall evaluations toward the
product? Does the overall evaluation reflect average experience or is it biased? In
this study, we focus on evaluating the integration bias in consumers’ rating
behavior through rating system design. Analysis of data from two leading
restaurant review websites with different rating systems suggests that the overall
ratings tend to reflect consumers’ extreme experiences in a single-dimensional
rating system, however, their average experience by taking all dimensions into
consideration in multi-dimensional rating systems. The results are confirmed by
information from text reviews through text mining skills.
4 - Webcasting Game Or Sharing Experience? Exploring The Role Of
Team-created Word-of-mouth In Football Game Attendance
Yang Wang, University of Utah,
Nick Sullivan, Shyam Gopinath
A 2015 NCAA report shows that college football attendance drops to the lowest in
15 years. To help generate demand, the 128 FBS teams develop different
strategies and use social media as a tool to attract fans. Among them, those
schools with top game attendance usually tweet a lot about game ambiance
which shows the unique game experience at the stadium, while the others only
webcast the team performance on the pitch. This study aims to examine the
differential impacts of the two types of team-created word-of-mouth on the
future game attendance versus the TV viewership. We find the unique role of
each type of the content and provide relevant business implications.
MD07
102B-MCC
Urban Data Analytics and Mining
Sponsored: Data Mining
Sponsored Session
Chair: Xun Zhou, University of Iowa, S210 PBB 21 East Market Street,
Iowa City, IA, 52242, United States,
1 - A Traffic Flow Approach To Early Detection Of Gathering Events
Amin Vahedian, University of Iowa, Iowa City, IA, United States,
Xun Zhou
Given traffic flows in a spatial field, early detection of gathering events problem
aims to discover the most likely gathering events. It is important for city planners
to identify emerging gathering events which might cause public safety or
sustainability issues. Here, we model the footprint of a gathering event as a
directed acyclic Graph, which captures routes of the flows to an event and their
most likely destination. We also propose an efficient algorithm to discover the
most likely events. Our analysis shows that the proposed model and algorithm
efficiently and effectively capture important gathering events from real-world
mobility data while saving 50% time over the baseline algorithm.
2 - Mapping The Structure Of China’S Cities Network
Xiaolong Xue, Harbin Institute of Technology,
The structure of China’s cities network is dramatical changing with the rapid
urbanization process. This paper analyzes the research status of cities network
theory, and constructs China’s cities network model using China’s transportation
infrastructure data. The structure of China’s cities network is described through
network characteristics, and China’s cities network is divided into different
network communities by clustering analysis. We find the center city, traffic hub
and regional centers by calculating cities nodes’ effectiveness. The calculating of
network effectiveness provides a reference for improving the efficiency of China’s
cities network.
3 - A Markov Decision Process Approach To Optimizing Taxi Driver
Business Efficiency
Xun Zhou, University of Iowa,
Improving taxi business efficiency is an important societal problem. This work
investigates how to increase the revenue efficiency (revenue per unit time) of taxi
drivers. To solve this problem we model the passenger seeking process as a
Markov Decision Process(MDP) and learn necessary parameters from historical
taxi data. A case study and several experimental evaluation on a real dataset from
a major city in China show that our proposed approach improve the revenue
efficiency of inexperienced drivers by up to 15%.
4 - Operation Strategies And Algorithms For Minibus Systems In
Hong Kong
Jacky Pak Ki Li, PhD Student, VU University Amsterdam,
De Boelelaan 1081a, Amsterdam, 1081 HV, Netherlands,
In Hong Kong, the spatial distribution of Minibuses within the public
transportation system is self-organized, lacking a clearly defined operation
strategy. There is no optimization based on current demand. Within this paper
several operation strategies are introduced. A new integrated algorithm for
optimal strategy is described in detail, including two approaches: a user-based
approach, outlining a strategy to capture and optimize consumer demand, and an
operation-based approach, outlining a strategy to balance revenue and consumer
satisfaction.
MD08
103A-MCC
Tutorial: Data-Driven Research in
Revenue Management
Invited: Business Model Innovation
Invited Session
Chair: David Simchi-Levi, Massachusetts Institute of Technology,
Masachusetts Avenue, Cambridge, MA, 0, United States,
1 - Data-Driven Research In Revenue Management
David Simchi-Levi, Massachusetts Institute of Technology,
77 Massachusetts Avenue, Cambridge, MA, United States,
We present a pricing optimization problem for the data plans of a big satellite
firm. First we address the problem of missing data (as reservation prices are not
directly observed especially for those who are not current customers). We
formulate the price optimization problem as a MIP and develop properties and
heuristics in order to solve realistic instances providing analytical lower bounds of
their performance. We conclude that with our method the company can increase
its profits by more than 10% and outperform the current plans’ prices even under
misspecifications of the assumptions.
MD06