What Are Recommendation Engines & How Do They Help Consumers?
You browse through your news feed or your favourite online and store. Next, you notice that one of your friends has liked a page you’d be interested in or purchased an item you like. Are you then suggested to like the same page or buy something similar to your friend?
But how did they know it would be suitable for you? Because of recommendation engines.
There is so much data being collected that finding a way of scanning through it and picking out the useful data has never been more relevant.
Recommendation engines allow this data to be filtered. The user on the other end is able to see the benefits because the only data that they see is tailored to them and their preferences.
Defining a recommendation engine
A recommendation engine is a piece of software that gives the user a list of selections based on the data it collects from their browsing preferences.
You will find a lot of recommendations when you browse online c-commerce stores. The site will be able to see what kind of books, clothes, films etc you like and use that data to suggest to you other items that you may like.
The most advanced recommenders using machine learning techniques to predict items that the user will like and work in an active environment.
There may be changes to an item that will mean the chances of a user selecting them to increase dramatically. This is particularly true in the retail sector when there is a sale so the recommendation engine will adapt.
A recommender system comes with the list by two methods: collaborative filtering and content-based filtering.
Collaborative filtering
This recommender system looks at the user’s previous behaviour to predict what items they may be interested in, based on other users with the same preferences.
Collaborative filtering has a key advantage in that it does not need to analyse the content of a listing or product to come up with an accurate suggestion.
However, collaborative filtering does have one major drawback. In order
to make accurate recommendations, a lot of data is required. If it has not
already been acquired, the predictions may be wide of the mark.
The method assumes that previous buyers, readers, etc will have the same taste as the current website user and that their older preferences will not have changed and will not change much going forward.
It creates a model using both implicit and explicit data, including
- how many times a user views the item;
- keeping records of what the user has purchased previously in the past;
- presenting the user with two options and making a note of their selection;
Aside from shopping, collaborative filtering is used by other large companies in popular sectors.
For example, Facebook, the largest social media company in the world, uses collaborative filtering, notably when suggesting to make new friends. It analyses who you have made connections with in the past, who your friends associate with and come with suggestions.
Spotify does the same with music. It recommends new artists or tracks to you based on what you have previously browsed and played.
Content-based filtering
This is another very common recommender system that uses the descriptions of an item to provide predictions, mainly using keywords.
An item is selected by the user. The system picks up on the selection, analyses it and comes with suggestions that best fit the same description.
The more information it can gather, the better the idea it has of the user and can provide more accurate recommendations.
In order for the system to know what
the characteristics of the item are, it creates an item profile.
Each characteristic of an item is given a value. The more a user searches for a specific keyword about an item, the more weighted that value becomes.
The recommendation engine will give suggestions more focused towards the higher weighted features.
Content-based filtering systems also base their recommendations on what the user rates highly. It will analyse the keywords from the content the user has shown to like and produce results based on these.
For example, YouTube videos have a like rating system where a user may say whether or not they like or dislike that video.
Based on what a user likes and dislikes, it will tailor the recommended content.
However, with all this comes an issue: can the system make accurate predictions using only one source of content and then using that information to cover all other types of content?
Content-based filtering can sometimes become quite limiting. Being able to recommend blog posts based on other blog posts makes sense, but suggesting podcasts, videos, forums would be even more useful.
Privacy concerns
Recommender systems have always been faced with problems over how they manipulate a user’s data. The more personal the information, the greater the chance that the user’s data privacy is being compromised.
In order to get the best results, users must provide the systems with highly sensitive and personal information.
These systems are able to collect and contain a very large amount of information about a user. If the security is not up to scratch, the information could get into the wrong hands.
Countries across the world have begun to restrict what data can be used and how it can be used. Most notably, the GDPR has recently come into practice. Failing to comply will result in severe punishments so it’s important that recommender engines comply.
Conclusion
Recommendation engines are used to provide assistance to a user in order to help them find other items they like. They help customers be more efficient in making decisions because the solutions are effectively given to them.
More and more businesses are going to want to start using this form of AI to become more competitive, meaning AI and humans collaborating to improve overall performance.
Recommendation systems can present a user with items or options that a user may not have been able to find. A normal search engine is not able to do that as they require specific inputs to give the user results.
