Choice modelling

Preference-rank translation

Preference-rank translation is a mathematical technique used by marketers to convert stated preferences into purchase probabilities, that is, into an estimate of actual buying behaviour. It takes survey data on consumers’ preferences and converts it into actual purchase probabilities. A survey might ask a question using a ranking scale such as : A marketing researcher will re-specify the numerical values during codification. 1 will become 5, 2 will become 4, 4 will become 2, 5 will become 1, and 3 will remain the same. In this way greater values will correspond with greater preference. Next, the researcher uses a data reduction technique like factor analysis to obtain aggregate scores. To convert these aggregate rankings into purchase probabilities, each category (in this case, each product) will be weighted with a translation coefficient. These weights are predefined. A typical weighting scheme is: The weighting schemes vary depending on the variables being measured. The following chart illustrates the procedure: Other purchase intention/rating translations include logit analysis and the intent scale translation. (Wikipedia).

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(New Version Available) Introduction to Voting Theory and Preference Tables

Updated Version: https://youtu.be/WdtH_8lAqQo This video introduces voting theory and explains how to make a preference table from voting ballots. Site: http://mathispower4u.com

From playlist Voting Theory

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Math for Liberal Studies - Lecture 2.2.1 Preference Lists

This is the first video lecture for Math for Liberal Studies Section 2.2: The Number of Candidates Matters. In this video, I discuss how we can use "preference lists" to allow voters to express their ranking of candidates in an election. We also see how knowing the preference lists can exp

From playlist Math for Liberal Studies Lectures

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Recommender Systems - Ranking Evaluation - Session 9

Evaluating ranking Mean average precision (MAP) Mean reciprocal rank (MRR) How to select the right metric?

From playlist Recommenders Systems (Hands-on)

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Evaluation 15: binary preference and Kendall tau

We can evaluate the search algorithm based directly on user clicks from the query log. Each click generates a set of preferences, and we can measure how well our ranking agrees with those preferences. The agreement can be measured with the Kendall tau coefficient, or with the binary prefer

From playlist IR13 Evaluating Search Engines

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Evaluation 10: recall and precision over ranks

Recall always increases with rank and is typically concave. Precision usually decreases with ranks and is typically convex. The crossover point is where precision and recall intersect, and is often (but not always) the point where the F-measure is maximised.

From playlist IR13 Evaluating Search Engines

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selection sort [imagineer]

selection sort tutorial

From playlist Get Ready for Coding Interview

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Unordered Selections (1 of 3: Relation to permutations)

More resources available at www.misterwootube.com

From playlist Working with Combinatorics

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How to translate video titles and descriptions - Simple quick & FREE method!

Increase visibility on YouTube by translating your titles/descriptions metadata into multiple languages. Improve SEO, boost Watch Time and reach a wider international audience. Translate video titles and descriptions into Russian, German, Hindi, French, Thai, Swedish, Norwegian, Portugu

From playlist All Videos from Mr Tompkins Ed Tech

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Chem 125. Advanced Organic Chemistry. 6. Stereoselectivity in the Aldol Reaction.

UCI Chem 125 Advanced Organic Chemistry (Spring 2016) Lec 6. Stereoselectivity in the Aldol Reaction View the complete course: http://ocw.uci.edu/courses/chem_125_advanced_organic_chemistry.html Instructor: James S. Nowick, Ph.D. License: Creative Commons BY-NC-SA Terms of Use: http://ocw

From playlist Chem125: Advanced Organic Chemistry

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Learning from ranks, learning to rank - Jean-Philippe Vert, Google Brain

Permutations and sorting operators are ubiquitous in data science, e.g., when one wants to analyze or predict preferences. As discrete combinatorial objects, permutations do not lend themselves easily to differential calculus, which underpins much of modern machine learning. In this talk I

From playlist Statistics and computation

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Pierre-Henri Chaudouard - 2/2 Introduction to the (Relative) Trace Formula

The relative trace formula as envisioned by Jacquet and others is a possible generalization of the Arthur-Selberg trace formula. It is expected to be a useful tool in the relative Langlands program. We will try to present the general principle and give some examples and applications. Pie

From playlist 2022 Summer School on the Langlands program

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Paradoxes of Liberty - Amartya Sen (1981)

Amartya Sen theoretically discusses the meaning of Liberty and problems inherent in its definition. This talk was given in 1981 at Queen's University in the Chancellor Dunning Trust Lecture series. 00:00 Talk 1:00:21 Questions #Philosophy #PoliticalPhilosophy #Ethics

From playlist Social & Political Philosophy

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Introduction to Dense Text Representation - Part 3

In the third part, I present advanced applications and training methods to learn dense text representations. Topics included: - Multilingual Text Embeddings - Data Augmentation - Unsupervised Text Embedding learning - Neural Search Slides: https://nils-reimers.de/talks/2021-06-Intro_Dens

From playlist Introduction to Dense Text Representation

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Unordered Selections (3 of 3: Applying to contexts)

More resources available at www.misterwootube.com

From playlist Working with Combinatorics

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3. The Birth of Algebra

(October 15, 2012) Professor Keith Devlin looks at how algebra, one of the most foundational concepts in math, was discovered. Originally presented in the Stanford Continuing Studies Program. Stanford University: http://www.stanford.edu/ Stanford Continuing Studies Program: https://cont

From playlist Lecture Collection | Mathematics: Making the Invisible Visible

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Kaggle Reading Group: On NMT Search Errors and Model Errors: Cat Got Your Tongue? | Kaggle

This week we'll be starting a new paper: "On NMT Search Errors and Model Errors: Cat Got Your Tongue?" by Felix Stahlber and Bill Byrne, published at EMNLP 2019. You can follow along with the paper here: https://www.aclweb.org/anthology/D19-1331.pdf About Kaggle: Kaggle is the world's lar

From playlist Kaggle Reading Group | Kaggle

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Ex 1: Find Domain and Range of Ordered Pairs, Function or Not

Given a relation as a set of ordered pairs, determine the domain and range. Then determine if the relation is a function. http://mathispower4u.com

From playlist Determining the Domain and Range of a Function

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Discrete Isometry Group of Higher Rank Symmetric Spaces (Lecture - 01) by Misha Kapovich

Geometry, Groups and Dynamics (GGD) - 2017 DATE: 06 November 2017 to 24 November 2017 VENUE: Ramanujan Lecture Hall, ICTS, Bengaluru The program focuses on geometry, dynamical systems and group actions. Topics are chosen to cover the modern aspects of these areas in which research has b

From playlist Geometry, Groups and Dynamics (GGD) - 2017

Related pages

Factor analysis | Questionnaire construction | Preference regression | Quantitative marketing research | Scale (social sciences)