In the contemporary digital landscape, users expect experiences tailored precisely to their expectations and needs. To enhance customer satisfaction, personalising the user experience—both in terms of content and offers—through recommendation systems is imperative.
A recommendation system, be it software or an algorithm, uses user profiling information to suggest content, products, or services aligned with user interests, tailoring the experience based on their preferences.
There are two broad types of recommendation system:
Collaborative Filtering, which focuses on variables such as behaviours, actions, preferences, and predictions derived from user affinities with similar users.
Content-Based Filtering, which concentrates on elements and content liked by the user, often based on characterising keywords, and considers the user’s choices.
Curating a successful personalisation strategy requires an in-depth understanding of the user. For example, Artificial intelligence plays a crucial role in adapting and improving customer experiences by efficiently analysing data to comprehend individual user expectations. AI algorithms aggregate and describe large amounts of data, including visit history, preferences of similar users, and the context of user interaction.
This analysis enables AI algorithms to provide indications that can help visitors choose which path to take, allowing them to view content aligned with their interests and preferences, optimise information searches, and avoid potentially uninteresting content.
For a recommendation system to perform well and provide accurate recommendations, it must be able to learn and be flexible and dynamic, exploiting real-time data and information on the type of users.
The collection and processing of user data with the goal of dividing users into groups or segments based on similar behaviours is known as profiling.
A highly effective method for profiling entails dividing the user database into more or less extensive clusters, creating homogeneous groups with shared characteristics.
Certain profiling and recommendation systems use an NLP classifier, a component of Natural Language Processing.
NLP, an interdisciplinary field spanning computer science, artificial intelligence, and linguistics, aims to develop algorithms capable of analysing, representing, and “understanding” natural written or spoken language in a manner comparable to, or even exceeding, human performance.
This understanding is achieved through the incorporation and use of language at various levels, ranging from words—considering their meaning and appropriateness of use—to grammar and structuring rules, from sentences derived from individual words and paragraphs to pages formed from sentences.
In more granular detail, NLP provides solutions for analysing syntactic structures by associating morphological categories (e.g., noun, verb, adjective) with individual words. It identifies entities, classifying them into predefined categories (e.g., person, date, place), and extracts syntactic dependencies (e.g., subjects and complements) and semantic relationships (e.g., hyperonymy, meronymy). In addition, NLP enables an understanding of text semantics by identifying word meanings based on context and methods of use (e.g., irony, sarcasm, feeling, mood). It classifies text into predefined categories (e.g., sport, geography, medicine) or summarises its content.
Modern NLP relies heavily on an approach to artificial intelligence called machine learning, employing a system that makes predictions by generalising examples into datasets. These training data are exploited by machine learning algorithms to produce a model and complete a task. The training data used for user profiling includes the text and responses gathered from questionnaires distributed to users to directly collect information about them. The machine learning algorithm reads this dataset and produces a list with statistical probability, or a single profile corresponding to the one with the highest probability identified.
This form of analysis can be used to create profiles of users based on their linguistic behaviour, interests and preferences. Once user profiles have been created using NLP, the information can be used to generate customised recommendations.
The Natural Language Process can be used to make ongoing improvements to the recommendation algorithm and to further improve the accuracy of recommendations. For example, it can be used to analyse user feedback and examine product descriptions and reviews to better understand user content and preferences. Combined with profiling, it can be useful to increase and improve the level of personalisation, thereby optimising the user experience and the effectiveness of recommendation platforms.
