science

Knowledge-based recommender system

Knowledge-based recommender system 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 Knowledge-based recommender system rather than just read about it. In short: Knowledge-based recommender systems (knowledge based recommenders) are a specific type of recommender system that are based on explicit knowledge about the item assortment, user preferences, and recommendation criteria (i.e., which item should be recommended in which context). These systems are applied in scenarios where alternative approaches such as collaborative filtering and content-based filtering cannot be app…

Key takeaways

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

Reference excerpt

Knowledge-based recommender systems (knowledge based recommenders) are a specific type of recommender system that are based on explicit knowledge about the item assortment, user preferences, and recommendation criteria (i.e., which item should be recommended in which context). These systems are applied in scenarios where alternative approaches such as collaborative filtering and content-based filtering cannot be applied. A major strength of knowledge-based recommender systems is the non-existence of cold start (ramp-up) problems. A corresponding drawback is a potential knowledge acquisition bottleneck triggered by the need to define recommendation knowledge in an explicit fashion.

Item domains Knowledge-based recommender systems are well suited to complex domains where items are not purchased very often, such as apartments and cars. Further examples of item domains relevant for knowledge-based recommender systems are financial services, digital cameras, and tourist destinations. Rating-based systems often do not perform well in these domains due to the low number of available ratings. Additionally, in complex item domains, customers want to specify their preferences explicitly (e.g., "the maximum price of the car is X") . In this context, the recommender system must take into account constraints: for instance, only those financial services that support the investment period specified by the customer should be recommended. Neither of these aspects are supported by approaches such as collaborative filtering and content-based filtering.

Conversational recommendation Knowledge-based recommender systems are often conversational, i.e., user requirements and preferences are elicited within the scope of a feedback loop. A major reason for the conversational nature of knowledge-based recommender systems is the complexity of the item domain where it is often impossible to articulate all user preferences at once. Furthermore, user preferences are typically not known exactly at the beginning but are constructed within the scope of a recommendation session.

Search-based recommendation In a search-based recommender, user feedback is given in terms of answers to questions which restrict the set of relevant items. An example of such a question is "Which type of lens system do you prefer: fixed or exchangeable lenses?". On the technical level, search-based recommendation scenarios can be implemented on the basis of constraint-based recommender systems. Constraint-based recommender systems are implemented on the basis of constraint search or different types of conjunctive query-based approaches.

Navigation-based recommendation In a navigation-based recommender, user feedback is typically provided in terms of "critiques" which specify change requests regarding the item currently recommended to the user. Critiques are then used for the recommendation of the next "candidate" item. An example of a critique in the context of a digital camera recommendation scenario is "I would like to have a camera like this but with a lower price". This is an example of a "unit critique" which represents a change request on a single item attribute. "Compound critiques" allow the specification of more than one change request at a time. "Dynamic critiquing" also takes into account preceding user critiques (the critiquing history). More recent approaches additionally exploit information stored in user interaction logs to further reduce the interaction effort in terms of the number of needed critiquing cycles.

See also Recommender system Collaborative filtering Cold start Case-based reasoning Constraint satisfaction Knowledge-based configuration Guided selling

References

External links Systems and datasets WeeVis Wiki-based Recommendation Environment Archived 2014-03-01 at the Wayback Machine VITA: Knowledge-based Recommender for Financial Services MyProductAdvisor Entree Dataset

Worked examples

Example 1 — a first encounter with Knowledge-based recommender system

Start with the simplest possible case. Write down what Knowledge-based recommender system 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 Knowledge-based recommender system 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 Knowledge-based recommender system 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 Knowledge-based recommender system

In research: Knowledge-based recommender system 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 Knowledge-based recommender system 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: Knowledge-based recommender system is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial intelligence, so understanding it makes those chapters shorter.
In everyday life: Look for Knowledge-based recommender system 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 Knowledge-based recommender system in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Knowledge-based recommender system 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 Knowledge-based recommender system out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Knowledge-based recommender system in simple terms?

Why does Knowledge-based recommender system 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 Knowledge-based recommender system?

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 Knowledge-based recommender system.