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A case study of behavior-driven conjoint analysis on Yahoo!: front page today module

Published: 28 June 2009 Publication History

Abstract

Conjoint analysis is one of the most popular market research methodologies for assessing how customers with heterogeneous preferences appraise various objective characteristics in products or services, which provides critical inputs for many marketing decisions, e.g. optimal design of new products and target market selection. Nowadays it becomes practical in e-commercial applications to collect millions of samples quickly. However, the large-scale data sets make traditional conjoint analysis coupled with sophisticated Monte Carlo simulation for parameter estimation computationally prohibitive. In this paper, we report a successful large-scale case study of conjoint analysis on click through stream in a real-world application at Yahoo!. We consider identifying users' heterogenous preferences from millions of click/view events and building predictive models to classify new users into segments of distinct behavior pattern. A scalable conjoint analysis technique, known as tensor segmentation, is developed by utilizing logistic tensor regression in standard partworth framework for solutions. In offline analysis on the samples collected from a random bucket of Yahoo! Front Page Today Module, we compare tensor segmentation against other segmentation schemes using demographic information, and study user preferences on article content within tensor segments. Our knowledge acquired in the segmentation results also provides assistance to editors in content management and user targeting. The usefulness of our approach is further verified by the observations in a bucket test launched in Dec. 2008.

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cover image ACM Conferences
KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
June 2009
1426 pages
ISBN:9781605584959
DOI:10.1145/1557019
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 June 2009

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Author Tags

  1. classification
  2. clustering
  3. conjoint analysis
  4. logistic regression
  5. segmentation
  6. tensor product

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  • (2024)Constrained contextual bandit algorithm for limited-budget recommendation systemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107558128(107558)Online publication date: Feb-2024
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