NDLI: Product Selection Problem: Improve Market Share by Learning Consumer Behavior

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ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 11

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 10

Issue 4(Special Issue on SIGKDD 2014, Special Issue on BIGCHAT and Regular Papers), July 2016

Introduction to the Special Issue of Best Papers in ACM SIGKDD 2014

Product Selection Problem: Improve Market Share by Learning Consumer Behavior

Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach

Heterogeneous Translated Hashing: A Scalable Solution Towards Multi-Modal Similarity Search

Guest Editorial: Special Issue on Connected Health at Big Data Era (BigChat): A TKDD Special Issue

Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis Using High-Dimensional Molecular Profiling Data

Jointly Modeling Label and Feature Heterogeneity in Medical Informatics

Mining Dual Networks: Models, Algorithms, and Applications

Biomedical Ontology Quality Assurance Using a Big Data Approach

Less is More: Building Selective Anomaly Ensembles

Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing

Inferring Dynamic Diffusion Networks in Online Media

Unsupervised Rare Pattern Mining: A Survey

CGC: A Flexible and Robust Approach to Integrating Co-Regularized Multi-Domain Graph for Clustering

Spatial-Proximity Optimization for Rapid Task Group Deployment

Featuring, Detecting, and Visualizing Human Sentiment in Chinese Micro-Blog

Eigen-Optimization on Large Graphs by Edge Manipulation

Issue 3, February 2016

Issue 2, October 2015

Issue 1, July 2015

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 9

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 8

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 7

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 6

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 5

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 4

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 3

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 2

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 1

Product Selection Problem: Improve Market Share by Learning Consumer Behavior

Content Provider	ACM Digital Library
Author	Lui, John C. S. Xu, Silei
Copyright Year	2016
Abstract	It is often crucial for manufacturers to decide what products to produce so that they can increase their market share in an increasingly fierce market. To decide which products to produce, manufacturers need to analyze the consumers’ requirements and how consumers make their purchase decisions so that the new products will be competitive in the market. In this paper, we first present a general distance-based product adoption model to capture consumers’ purchase behavior. Using this model, various distance metrics can be used to describe different real life purchase behavior. We then provide a learning algorithm to decide which set of distance metrics one should use when we are given some accessible historical purchase data. Based on the product adoption model, we formalize the k most marketable products (or $k-\textbf{MMP})$ selection problem and formally prove that the problem is $\textit{NP-hard}.$ To tackle this problem, we propose an efficient greedy-based approximation algorithm with a provable solution guarantee. Using submodularity analysis, we prove that our approximation algorithm can achieve at least 63% of the optimal solution. We apply our algorithm on both synthetic datasets and real-world datasets (TripAdvisor.com), and show that our algorithm can easily achieve five or more orders of speedup over the exhaustive search and achieve about 96% of the optimal solution on average. Our experiments also demonstrate the robustness of our distance metric learning method, and illustrate how one can adopt it to improve the accuracy of product selection.
Starting Page	1
Ending Page	25
Page Count	25
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/2753764
Volume Number	10
Issue Number	4
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-06-29
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Product selection Approximation algorithm Consumer behavior Model learning Submodular set function
Content Type	Text
Resource Type	Article
Subject	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

Product selection problem: improve market share by learning consumer behavior

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Product Selection Problem: Improve Market Share by Learning Consumer Behavior

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