science

Preference elicitation

Preference elicitation is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Preference elicitation rather than just read about it. In short: Preference elicitation refers to the problem of developing a decision support system capable of generating recommendations to a user, thus assisting in decision making. It is important for such a system to model user's preferences accurately, find hidden preferences and avoid redundancy.

Key takeaways

Preference elicitation belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Preference elicitation to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Preference elicitation from memory before moving on to harder problems.

Reference excerpt

Preference elicitation refers to the problem of developing a decision support system capable of generating recommendations to a user, thus assisting in decision making. It is important for such a system to model user's preferences accurately, find hidden preferences and avoid redundancy. This problem is sometimes studied as a computational learning theory problem. Another approach for formulating this problem is a partially observable Markov decision process. The formulation of this problem is also dependent upon the context of the area in which it is studied.

Overview With the explosion of on-line information new opportunities for finding and using electronic data have been generated, these changes have also brought the task of eliciting useful information to the forefront. Researchers as well as major online catalog companies have come up with algorithms and prototypes of systems that can aid a user to be able to navigate through a complex and huge information space using some information from the user in the form of answers to certain queries or ratings to certain items etc. depending upon the domain of the information space.

See also Active learning (machine learning) Cold start Collaborative filtering Collective intelligence Long tail Personalized marketing Product finders Revealed preference

External links Special Issue on Preferences. AI Magazine Vol 29, No 4: Winter 2008

Worked examples

Example 1 — a first encounter with Preference elicitation

Start with the simplest possible case. Write down what Preference elicitation claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Preference elicitation before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Preference elicitation ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Preference elicitation

In research: Preference elicitation appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Preference elicitation in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Preference elicitation is common in secondary-school and first-year university syllabi. It links to neighbouring topics Decision support systems, Information systems, Recommender systems, so understanding it makes those chapters shorter.
In everyday life: Look for Preference elicitation outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Preference elicitation in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Preference elicitation means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Preference elicitation out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Preference elicitation in simple terms?

Why does Preference elicitation matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Preference elicitation?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Preference elicitation.