Recommendations deliver content that is fresh, popular, related and customised to user interests and preferences.
Improve your users’ engagement by keeping them exploring and discovering content with recommendations. By getting relevant content suggestions, a user would feel satisfied your site understands their needs and interests. This can ultimately help you reduce bounce rates and increase conversion rates.
The system learns from real-time user behaviour and runs on advanced algorithms to personalise content suggestions.
Displays content a user recently interacted with and supplements it with suggestions of content similar to what they’re interested in.
Analyses user behaviour and recommends content similar to what other users with similar tastes have enjoyed.
Displays recently released content and supports it with trending content that's garnered the most engagement.
The system can be customised to specific business needs and goals, including different recommendation types and layouts.
The Recommendations feature in Convivial for GovCMS delivers content that is fresh, popular, related, and tailored to individual user interests and preferences. By surfacing relevant content suggestions, it keeps users exploring and discovering information on government websites. This helps reduce bounce rates and increase conversion rates, ensuring citizens spend more time engaging with content that matters to them, rather than leaving the site without finding what they need.
Convivial for GovCMS uses Recombee, a leading AI-powered Recommender as a Service, to deliver intelligent content suggestions. Morpht has partnered with Recombee and developed a dedicated Drupal module to integrate its capabilities into the GovCMS platform. The system tracks real-time user interactions with site content and applies advanced algorithms to personalise recommendations, similar to the approach used by platforms such as Amazon, Netflix, and Spotify, but tailored for government digital services.
Convivial for GovCMS supports four types of content recommendations. Recent and similar content shows items a user has previously interacted with alongside related suggestions. Collaborative filtering recommends content enjoyed by users with similar tastes, such as the latest and popular surfaces, newly published, and trending content. The system also supports customisation to meet specific agency goals, including different recommendation types and layouts, giving government content strategists flexibility in how recommendations are presented across their sites.
Collaborative filtering is a recommendation method that analyses the behaviour of multiple users to identify shared patterns and preferences. In Convivial for GovCMS, it recommends content to a user based on what other users with similar interests have engaged with, leveraging the collective behaviour of the entire user base. For government agencies with large and diverse audiences, this approach helps surface relevant content that individual users may not have discovered on their own.
Yes. The recommendations system in Convivial for GovCMS is designed to be customised to specific business needs and goals. Government agencies can configure different recommendation types and adjust layouts to align with their content strategy and audience requirements. Because the system learns continuously from real-time user behaviour and runs on advanced algorithms, it improves over time, making it a scalable solution for agencies looking to deliver increasingly relevant and personalised digital experiences to citizens.
