Utilizing Machine Learning

In our pursuit of ensuring the digital safety of users, one crucial task is the correct and detailed categorization of websites. This article offers a glimpse into the traditional methodologies employed for website categorization and the ongoing enhancements therein.
At cyan, our objective is to safeguard end-users throughout their online engagements. Achieving this requires profound comprehension of the vast expanse of online content. We work on deciphering the nature of online content and try to establish whether any protective measures are warranted. Whether catering to a tech-savvy individual wary of downloading malicious files, parents seeking to shield their children from inappropriate content, or safeguarding individuals against fraudulent online entities, our aim is to deliver optimal protection. Thus, the precise collection and categorization of websites is of utmost significance.
The internet’s exponential growth, coupled with the expansion of our user base, underscores the escalating complexity of website categorization. The sheer volume of content presents a tough challenge, compounded by its dynamic nature as the internet evolves globally.

Classical Approaches
Historically, website categorization has relied on two primary strategies. The first, manual categorization, entails individual assessment by human evaluators assigning websites to predefined categories. While yielding reliable results, this approach lacks scalability, especially in light of the staggering influx of new domains daily.

Alternatively, rule-based categorization involves the formulation of criteria to classify websites fast. For instance, identifying adult content based on domain names containing explicit terms. While efficient, rule-based systems are receptive to false positives and negatives, undermining their efficiency.

At cyan, we have employed these techniques for over 15 years, effectively catering to our clients’ linguistic markets. Nevertheless, as we expand into new territories, our methodologies evolve. Thus, we have integrated Machine Learning (ML) techniques to boost our categorization efforts.

Machine Learning
ML revolutionizes categorization by leveraging data over code, enabling algorithms to discern patterns autonomously. By furnishing algorithms with a repository of websites and their respective categories, ML facilitates the derivation of intricate classification rules.
We are actively exploring various ML methodologies for website classification. Currently, our focus lies in analyzing webpage content, encompassing text and images, to enhance categorization accuracy.

In conclusion, our mission to enhance website categorization using Machine Learning reflects our unwavering dedication to safeguarding users across the globe. We’re excited to share our progress with you and invite you to stay connected as we continue to explore new horizons in online safety. Let’s create a safer tomorrow together and ensure that we all can navigate the digital world with confidence.