Part 4 of 4 - How Machine Learning Powers an AI-enabled Service? (ML Algorithms in a Recommendation System)

In the previous article, we looked at the example of a great user experience created during booking a stay at a Semi Luxury hotel using an AI-based recommendation system.

We saw how his experience is enhanced through a series of steps taken by the recommendation system behind the scenes – data collection, data pre-processing, user profiling, analysis and insights, contextualization, recommendation generation, delivery of recommendations, feedback collection, and continuous improvement.

This AI-based recommendation system relies on Machine Learning to carry out its intelligent tasks of user profiling, analyzing, contextualizing, and generating recommendations.

In a Semi-Luxury Hotel’s recommendation system, the ML algorithms analyze a guest’s profile and the context of his stay. If he’s booked during spring and previously enjoyed outdoor activities, the system, using pattern recognition and predictive modeling, recommends a guided nature hike available that weekend, considering the pleasant weather forecast.

Let’s see how machine learning algorithms power the recommendation system AI for the semi-luxury chain of hotels,

1. ML Algorithms in Data Preprocessing and Analysis Stage

Pattern Recognition: To cater to data processing and analysis ML algorithms analyze historical and current booking data to identify patterns and trends in guest preferences and behavior. For instance, clustering algorithms can group guests with similar preferences to discover common interests or needs.

When Vijay’s profile is analyzed, the ML algorithm finds that it belongs to one or more of these clusters.

Natural Language Processing (NLP): The recommendation system can make use of Feedback, review, and social media text to extract interests and sentiments.

NLP techniques extract sentiment and specific interests or requirements mentioned by guests. This helps in understanding nuanced preferences beyond structured data. NLP techniques utilize Machine Learning algorithms.

2. ML Algorithms during the User Profiling Stage

Predictive Modeling: Predictive models use guests’ historical data to forecast future preferences and likely interests. For example, if a guest like Vijay has shown a growing interest in wellness activities, a model might predict his interest in new spa services or yoga classes.

Dynamic Profiling: ML algorithms continuously update guest profiles based on new data, ensuring that recommendations remain relevant and personalized over time.

3. ML Algorithms during the Recommendation Generation Stage

Collaborative Filtering: This ML technique recommends items by finding similar users (user-based) or similar items (item-based). For a guest planning a new booking, the system might suggest activities that other guests with similar profiles enjoyed.

Content-Based Filtering: This ML approach recommends items based on the features of items and a user profile. If Vijay likes outdoor adventures, the system might recommend hiking or biking based on the ‘outdoor’ feature of these activities.

Hybrid Methods: Our AI system combines collaborative and content-based filtering, giving us what is called a hybrid ML method offering more accurate and diverse recommendations, tailoring suggestions to the unique preferences of each guest.

4. ML Algorithms during the Contextualization and Personalization Stage

Context-Aware Recommendations: The contextualization ML model can consider real-time contextual information, such as local events or weather conditions, to adapt recommendations. For instance, the model can recommend indoor activities on rainy days or special local events during the guest’s stay.

Personalization Algorithms: These algorithms use data from the user profiles and the contextualization process to rank and suggest the most relevant services, activities, or promotions to each guest.

5. ML Algorithms used in Feedback Loop

Reinforcement Learning: This area of ML allows the system to learn directly from interaction with guests—adjusting recommendations based on feedback and engagement. Positive feedback on a recommendation reinforces similar future suggestions, while negative feedback adjusts the model to avoid similar recommendations.

Continuous Learning: ML models are retrained on updated datasets including new guest feedback, preferences, and behaviors, allowing the system to evolve and improve its recommendation accuracy over time.

In summary, ML is integral to every step of the recommendation system, from analyzing data and generating personalized recommendations to refining the process based on guest feedback, ensuring each guest’s experience is as enjoyable and tailored to their preferences as possible.

With this, we come to the end of a series of 4 articles that help business leaders understand how AI works for a service business.

In the first article, we explored the need to understand business data. Data is fuel for AI. Through a survey, we help you explore your data. https://aiandmlsimplified.com/how-to-discover-potential-of-ai-in-business-first-step/ 

In the second article, we showed how to go about selecting an AI implementation using identified data and business goals. We should what kind of business and functional analysis goes into envisioning an AI system. https://aiandmlsimplified.com/how-to-envision-an-ai-enabled-service/ 

In the third article, we showed how the AI system works in delivering value. https://aiandmlsimplified.com/how-does-an-ai-enabled-service-work/