Unlocking Personalisation: The Impact of Recommendation Engines

Home Data Science Unlocking Personalisation: The Impact of Recommendation Engines
A robot passing a box to a robotic arm

Imagine you’re scrolling through your favourite online store or hunting for a new series to watch on Netflix. Suddenly a subtle force takes the reins, guiding you towards something perfectly tailored to your tastes. Step into the realm of recommendation engines, the digital guides shaping your daily online journeys. These dynamic data filtering systems have a huge influence on internet users, and they are gradually becoming more complex and accurate.

They not only supercharge your searches for products and services but also elevate your overall online experience, all while boosting business for the companies behind them. Recommendation engines take different forms for unique purposes, with the primary types being collaborative and content-based.

What is Collaborative Filtering?

We can think of this as user-based filtering. Collaborative filtering recommendation engines use a diverse set of user data as their input, including your own unique imprint. Over time, this data weaves together your digital interactions, opinions and/or characteristics and finds your ‘digital soulmates’ – users who share similar online profiles. Then, the engine suggests things you might love based on what those like-minded users have enjoyed. You’ve likely seen these suggestions on shopping platforms under sections like “Similar customers also purchased…”.

Methods of Collaborative Filtering

1. Memory-Based Collaborative Filtering

Firstly, memory-based collaborative filtering (also known as neighbourhood-based) makes recommendations based on the entire dataset of users/items. This method assumes that pairs of users who agreed on items in the past will continue to agree on future items. We can determine the level of this agreement between two users using algorithms like Pearson Correlation Coefficient and Cosine Similarity.


  • Simple and transparent, making it easy to understand.
  • Potential to offer suggestions unrelated to previous interactions.


  • Performance decreases as the dataset expands, causing scalability issues.
  • Struggles to handle new items effectively.

2. Model-Based Collaborative Filtering

Secondly, the model-based approach uses various machine learning algorithms like clustering models, Bayesian networks and deep-learning techniques to identify complex patterns within the data. We can use these to predict users’ ratings of unseen items. Hence, newly added items don’t get lost in the system.


  • Addresses scalability issues by using dimensionality reduction which reduces large, sparse matrices into more meaningful small matrices.
  • Resolves the problem of handling new items effectively.


  • Requires more time and skill compared to memory-based approaches.
  • Inner workings of the system are harder to track, making the reasoning behind suggestions less transparent.

As there are strengths in both memory-based and model-based methods, many recommendation systems using collaborative filtering employ a hybrid of the two. However, even the hybrid has disadvantages like:

  • It is only effective for well-established businesses with extensive user interaction history.
  • It works under the assumption that there are people in their system that have very similar tastes/requirements to you in every category. This is often unrealistic as everyone’s tastes, wants and needs are unique to them.

What is Content-Based Filtering?

While collaborative filtering builds bridges between users based on shared preferences, content-based filtering takes a different approach, basing its recommendations on the intrinsic features of items or services. Therefore, it is keenly attuned to your unique tastes and preferences.

Unlike collaborative filtering, content-based systems operate independently of other users’ data. Instead, they meticulously examine the unique features of items, tailoring suggestions based on your past interactions. It usually does this using cosine similarity, like the memory-based collaborative filtering, but calculating the similarity between features of items instead of users.


  • High degree of personalisation as this method is only based on your own unique preferences.
  • It doesn’t rely on a history of interactions from other users. This independence makes content-based systems well-suited for scenarios where user data might be sparse or challenging to obtain, like in a start-up business.


  • The system requires features to be hand-engineered, which involves manually selecting the features or carrying out substantial pre-processing. This can be a labour-intensive and time-consuming process.
  • While not dependent on other users’ interactions, content-based systems still need a history of your own interactions to return optimal results. It’s important to note that this can also be viewed as an advantage as you have full control over the accuracy of your own suggestions. For example, to improve your recommendations on a streaming service, you can watch more of the movies/series you like and ensure that you rate them and anything else that you have already seen – more interactions give more accurate results.


  • In a moisturiser, the said features might be the ingredients, so the system could suggest products containing similar ingredients. This can help the user to find a product that’s better value for money.
  • The features of a movie could be the genre, actor names or release year. A streaming service can use your list of previously watched movies to find similar movies based on these features.


In the world of online experiences, recommendation engines play a crucial role. Whether connecting similar users or tailoring suggestions to individual tastes, these engines significantly impact our online experiences. They not only refine searches but also shape the success of online businesses.

So, next time you receive a suggestion that feels like it knows you better than you know yourself, remember the intricacies of these processes working behind the scenes. These unseen guides continuously innovate to enhance our digital journeys.

What are your thoughts or experiences of recommendation engines? Feel free to let us know in the comments below!

Or contact us today if you think your business could benefit from the addition of a recommendation engine.

Erin Ward

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