science

Location-based recommendation

Location-based recommendation 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 Location-based recommendation rather than just read about it. In short: Location-based recommendation is a recommender system that incorporates location information, such as that from a mobile device, into algorithms to attempt to provide more-relevant recommendations to users. This could include recommendations for restaurants, museums, or other points of interest or events near the user's location.

Key takeaways

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

Reference excerpt

Location-based recommendation is a recommender system that incorporates location information, such as that from a mobile device, into algorithms to attempt to provide more-relevant recommendations to users. This could include recommendations for restaurants, museums, or other points of interest or events near the user's location. These services take advantage of the increasing use of smartphones that store and provide the location information of their users alongside location-based social networks (LBSN), like Foursquare, Gowalla, Swarm, and Yelp. In addition to geosocial networking services, traditional online social networks such as Facebook and Twitter are using the location information of their users to show and recommend upcoming events, posts, and local trends. In addition to its value for users, this information is valuable for third-party companies to advertise products, hotels, places, and to forecast service demand such as the number of taxis needed in a part of a city.

Background Recommender systems are information filtering systems which attempt to predict the rating or preference that a user would give, based on ratings that similar users gave and ratings that the user gave on previous occasions. These systems have become increasingly popular and are used for movies, music, news, books, research articles, search queries, social tags, and products in general.

Recommending new places The main objective of recommending new places is to provide a suggestion to a user to visit unvisited places like restaurants, museums, national parks or other points of interest. This type of recommendation is quite valuable, especially for those who are traveling to a new city and want the best experience during their trip. Location-based social networks or third-party advertising companies are willing to provide a recommendation not only based on previous check-ins and preferences but also using social links to suggest a not-visited point-of-interest. The implicit goal of this type of recommendation is to lift the user's burden of searching for an interesting place. One of the first studies in this area was conducted in 2011. The idea behind this work was to leverage social influence and location influence and provide recommendations. The authors provide three types of scores:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Location-based recommendation

Start with the simplest possible case. Write down what Location-based recommendation 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 Location-based recommendation 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 Location-based recommendation 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 Location-based recommendation

In research: Location-based recommendation 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 Location-based recommendation 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: Location-based recommendation is common in secondary-school and first-year university syllabi. It links to neighbouring topics Internet geolocation, Recommender systems, Social information processing, so understanding it makes those chapters shorter.
In everyday life: Look for Location-based recommendation 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 Location-based recommendation in 20 minutes

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

Frequently asked questions

What is Location-based recommendation in simple terms?

Why does Location-based recommendation 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 Location-based recommendation?

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 Location-based recommendation.