A Brief Introduction to Machine Learning Personalization Models

Did you know that 74% of customers feel frustrated when a website content is not personalized? Did you know that 57% of people don’t mind sharing their personal information as long as it benefits them and is used responsibly? Businesses have infused personalization with machine learning and the result is eye-opening.

Personalization based on machine learning is an accurate and scalable technique to create unique experiences for each customer. Rather than using rule-based personalization to segment customers, it allows us to employ algorithms to deliver these one-to-one experiences.

Collaborative Filtering Model

Collaborative filtering is one of the numerous models for individualized content recommendations. As the name suggests, based on similar tastes and preferences, relevant content is put forth. For instance, if a cyclist and the businessman have given similar review scores to numerous homes, this could mean that both shares similar interests and feedback. Thus, if any one of them has given a score of ‘4’ on a property, there’s a higher probability for the other to score the same. You can display your suggestion accordingly thereby making your users realize that you ‘understand’ them. This is a great machine learning personalization model for your business to attract customers.

Classification Model

To determine a match, you look at the features of known groupings and apply those traits to fresh data. Depending on the data you have and the type of conclusion you seek, you can utilize a variety of classification types. This machine learning personalization model can be used to find content that corresponds to a customer's tastes. It can also be used to group customers into profile groups based on profile images, communication patterns, and other factors.

Association Rule Learning Model

You can use Association Rule Learning Model to figure out how elements in data collection are related. It can be used to determine the direction of a relationship between two numbers. For instance, if a customer buys item A, they are likely to buy item B as well. However, the converse of it may not be true. This is mostly used in product/service recommendations. Try out this machine learning personalization model for your business!

Reinforcement Learning Model

Reinforcement learning is a type of machine learning model in which data is quite actively collected and applied by a set of algorithms. Analysts must balance the collection of relevant data with the consistent application of predictions when using reinforcement models. By allowing some unrelated recommendations to customers through this machine learning personalization model you can obtain more customer inputs. The goal is to improve accuracy and embrace a greater range of contextual data for better user experience.

Sequence-aware Recommenders Model

Have you realized that when you’ve booked some travel destinations you are recommended a few others quite like your plan? This is mostly due to the Sequence-aware recommender that reads your preferences and knows when to recommend a domestic or an international travel destination. It considers the context in play and smartly suggests what may attract you further.

Machine learning is a combination of art and science. There is no universal answer or method for adopting machine learning personalization models. Several things can influence your decision. So, decide wisely considering the gathered data. Ameex has assisted a globally-renowned health, hygiene and nutrition provider with Machine Learning based Personalization tactics. Their clickthrough rates have increased phenomenally for recommended articles, videos etc. Boost your revenue and margins now with us.