The exponential growth of digital connection in our world is all-pervasive and touches on every aspect of our daily lives, both personally and professionally.
Today, many people would be hard-pressed to imagine life before the advent of digital technology. It has blended and integrated seamlessly into everyday living. Global interconnectivity and the ability to communicate and network instantly is now a ‘given’. This expectation has markedly transformed the human experience across all areas, and the ‘always on’ culture has led to the creation of a vast online and computerised world.
Creatively, this has inevitably resulted in phenomenally new ways of working and interpreting the world through data. Artificial intelligence (AI), and its attendant strands of computational science, are a vital link in the chain of twenty-first century life.
Whose choice is it anyway?
As the everyday world becomes saturated with digital information, the element of choice and decision-making becomes harder to navigate. The sheer amount of data available has led to the development of programs to enable and equip end-users to make such choices bespoke. Examples can be simplistic – from choosing the best shampoo to suit your hair type or which restaurant to choose for a special night out – or, more complexly, looking for a new home in a different area.
Recommender systems are built into many technological platforms and are used for both individual and ecommerce purposes. Although choice availability appears straightforward, the process behind it is remarkably elaborate and sophisticated.
The science behind the experience
The recommender system is an information filtering system run by machine learning algorithms programmed to anticipate and predict user interest, user preferences and ratings in relation to products and/or information browsed online.
Currently, there are three main types of recommender systems:
- Content-based filtering. This is driven and influenced by user behaviour, and picks up what has been previously or currently searched for. Keyword-dependent, it seeks out the filtering approach and patterns regarding items in order to inform decision making.
- Collaborative filtering recommender. This uses a more-advanced approach in that similar users are selected based on their choice of similar items. Collaborative filtering methods are centred on analysing the importance of user interactions on like-for-like user items and common selections, enabling comparisons to be made.
- Hybrid recommender. An amalgam of the two previous types, this system creates a hybrid once recommended items have been generated.
The optimal functionality of recommendation engines depends upon information and raw data extracted from user experience and user ratings. When combined, these facilitate the building of user profiles to inform ecommerce targets and aims.
Multiple commonly accessed corporations and e-markets are highly visible and instantly recognisable on the online stage. Household names such as Amazon and Netflix are brands that immediately spring to mind. These platforms invest massively in state-of-the-art operations and big data collection to constantly improve, evolve and calibrate their commercial aims and marketing.
Computer architecture and system software are predicated on a myriad of sources and needs, and rely heavily on machine learning and deep learning.These two terms are often considered interchangeable buzzwords, but deep learning is an evolution of the former. Using programmable neural networks, machines have the ability to make accurate and precise decisions without human intervention. Within the machine learning environment, the term ‘nearest neighbour’ is an essential classification algorithm – not to be confused with its traditional association in the pre-computer era.
Servicing enabling protocols, technologies and real-world applications requires in-depth skills and knowledge across multiple disciplines. By no means an exhaustive list, familiarity with, and indeed specialist awareness of, the following terms are integral to the optimisation of recommendation algorithms and the different types of recommendation models:
- Matrix factorization. This refers to the collaborative filtering algorithms used in recommender systems. New user items are decomposed into the product of two lower-dimensionality, rectangular matrices. Mathematical modelling further splits these entities into smaller entries in order to discover the features or information leading to interactions between different users and items. Once alerted by the search engine, matrix factorization generates product recommendations.
- Cold-start problem. This is an issue which presents in both supervised and unsupervised machine learning and is frequently addressed.
- Cosine similarity. Needing to determine the nearest user to provide recommendations, this is an approach to measure similarities between two non-zero vectors.
- Data sparsity. Many commercial recommender systems are based around large datasets. As such, the user-item matrices used in collaborative filtering could be large and sparse. Therefore, such data sparsity could present a challenge in terms of optimal recommendation performance.
- Data science. IBM’s overview offers a comprehensive explanation, and introduction to, the employment of data science within its use of data mining and complex metadata.
- Programming languages. Globally used programming languages include Scala, Perl, SQL, C++ and Python. Python is one of the foremost languages used. Managed by Grouplens Research at the University of Minnesota, MovieLens makes use of Python in collaborative filtering. Its programme predicts film ratings based on user profiles, plus user ratings, and overall user experience.
What’s happening with social media?
In recent years, recommender systems have become integral to the continued growth of social media. Due to the nature of the interconnected online community across locations and social demographics, a higher volume of traffic is both generated and triggered by recommendations enforced by likes and shares.
Online shopping has exploded as a result of the global pandemic. Websites such as Meta (formerly Facebook) and Etsy have been joined by new e-businesses and ‘shop fronts’, all of which incorporate the latest recommender technology. Targeted focus centres on growing user profiles by analysing purchase history and the browsing of new items. The aim is to both attract new users and retain existing ones. These embeddings are made possible through the use of recommender systems.
Careers in artificial intelligence and computer science
Professionally relevant associations such as the Institute of Electrical and Electronics Engineers (IEEE), and digital libraries such as Association for Computing Machinery (ACM), exist to provide further knowledge and support to those working in this fascinating field.
Whichever specialisation appeals – computer science, software development, programming, AI-oriented solutions development – the many pathways are leveraged to build a rewarding career. There are many in-demand roles and no shortage of successful and creative organisations in which to work as evidenced in 50 Artificial Intelligence Companies to watch in 2022.
Further your learning in this fast-paced field
If you’re looking for a university course offering up-to-date theoretical and practical knowledge with holistic, pedagogical and real-world expertise, then choose the University of York’s online MSc Computer Science with Artificial Intelligence course and take your next step towards a fulfilling and stimulating career.