ADAPT Next-Generation Recommender

A collaborative, contextual and content-based recommender

Industry Challenge

In 2017, Ryanair was launching a new accommodation service – Ryanair Rooms. As this was a new service, Ryanair had limited data on guests using this new service. Ryanair wanted to explore how it could offer a more personalised experience to these customers. The two main approaches to recommender systems are Collaborative Filtering and Content-based approaches, which do not perform well where there is limited data. Following an innovation workshop with ADAPT, the company decided to engage in a collaborative project to explore a novel room recommender system.

– Data sparsity problem and lack of personalisation in Collaborative Filtering approaches
– Content-based approaches suffer from over-specialisation
– Current approaches ignore user’s context
– Addition of new user or items is time and resource-consuming

ADAPT Solution

A real-time hybrid recommender that combines different techniques and exploits all the available information about users, such as:
User’s preferences to personalise recommendations
Group preferences to capture tastes of similar people
Data associated with items to apply content-based techniques
Contextual information

It overcomes the user and item cold start problems and overcomes the shortcomings of content-based and collaborative filtering approaches.

Inputs: User’s preferences, contextual information.

Technology: Hybrid approach that blends elements of naïve collaborative filtering, content-based recommendation and contextual suggestion.

Outputs: Custom recommendations.

ADAPT Next-Generation recommender system.

Results & Benefits 

Does not require significant rating data.
Generates personalised recommendations.
Provides real-time and robust recommendations.
Recommendations for Users in cold start contexts: few (or no) data about users.

Want to learn more?

Check out this video by Prof. Conlan’s on his Personalisation Research

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