Information relationships

Profile

Profile

Profile

Profile

Profile

Profile

Profile

Profile

Link 9

Link 10

Information relationships

Profile

Profile

Profile

Profile

Profile

Profile

Profile

Profile

Link 9

Link 10

Information relationships

Profile

Profile

Profile

Profile

Profile

Profile

Profile

Profile

Link 9

Link 10

Related content suggestions

Related content suggestions are recommendations for additional content or resources that are relevant to what the user is currently viewing or interacting with. This pattern helps users discover new, relevant information and can increase engagement with your platform.

Benefits and Use Cases
  • Encourages content discovery. Users can find relevant content they might not have searched for explicitly.

Example

In Cluster, suggest related content clusters based on the topics and tags of a user's current project.

  • Increases user engagement. By providing relevant suggestions, users are more likely to spend more time on the platform.

Example

At the bottom of a content piece in Cluster, show related articles or resources from the same or related clusters.

  • Supports learning and research. Related content can help users explore a topic more deeply or from different angles.

Example

When a user is viewing an AI-generated summary in Cluster, suggest other content pieces that cover related subtopics or offer different perspectives.

  • Improves content organization. By linking related content, you create a more interconnected and coherent information structure.

Example

Use related content suggestions in Cluster to create thematic links between different clusters, helping users see connections across their projects.

Psychological Principles Supported
  • Curiosity Gap. Well-crafted related content suggestions can create a sense of curiosity, encouraging users to explore further.

Example

In Cluster, use intriguing titles or brief teasers for related content suggestions to spark user interest.

  • Cognitive Momentum. Once users start exploring a topic, they're more likely to continue if presented with relevant, interesting options.

Example

As users navigate through related content in Cluster, dynamically update suggestions based on their browsing path to maintain relevance and encourage continued exploration.

  • Implicit Learning. Users can gain a broader understanding of a topic by being exposed to related content, even if they don't explicitly seek it out.

Example

In Cluster's content viewing interface, subtly highlight key terms that appear across related content pieces, helping users implicitly grasp important concepts or trends in their field.

Implementation Guidelines

DON'T

Overwhelm users with too many related content suggestions at once

Suggest content that is too similar to what the user is already viewing

Ignore the context in which related content is being suggested

Make related content suggestions too prominent that they distract from the main content

Forget to provide options for users to refine or opt out of content suggestions

DO

Use algorithms that consider multiple factors (e.g., content similarity, user behavior, popularity) to generate relevant suggestions

Provide clear, concise explanations for why content is being suggested

Allow users to easily navigate between related content items

Regularly update and refine your recommendation algorithms based on user interactions

Ensure related content suggestions are diverse to avoid creating echo chambers