Personalized recommendations
Personalized recommendations involve suggesting content, features, or actions to users based on their behavior, preferences, or characteristics. This pattern aims to enhance user engagement by proactively offering relevant options and guiding users towards valuable experiences within the application.
Benefits and Use Cases
Improves 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.
Enhances user engagement. Relevant recommendations can encourage users to explore more of the platform's features.
Example
Recommend new Cluster features or tools based on the user's role and usage patterns.
Supports decision-making. Personalized suggestions can help users make choices in complex scenarios.
Example
Suggest optimal AI settings in Cluster based on the user's content type and past preferences.
Facilitates connections. Recommendations can help users find valuable collaborations or resources.
Example
Recommend potential team members or experts in Cluster based on project topics and user networks.
Psychological Principles Supported
Curiosity Gap. Well-crafted recommendations can create a sense of curiosity, encouraging users to explore further.
Example
In Cluster, tease recommended content with intriguing titles or snippets to spark user interest.
Social Proof. Recommendations based on peer behavior can leverage the principle of social influence.
Example
Highlight popular clusters or content in Cluster with labels like "Trending in Your Network" to encourage engagement.
Endowment Effect. Personalized recommendations can make users feel that the suggested content is especially valuable to them.
Example
Frame Cluster recommendations as exclusive or specially curated, e.g., "Handpicked for You Based on Your Expertise."
Implementation Guidelines
DON'T
Overwhelm users with too many recommendations at once
Rely solely on past behavior, which may limit discovery of new interests
Make recommendations that could be seen as invasive or overly presumptuous
Ignore the context in which recommendations are being made
Fail to provide options to opt out of personalized recommendations
DO
Base recommendations on a combination of user behavior, stated preferences, and contextual relevance
Provide clear explanations for why items are being recommended
Allow users to easily dismiss or refine recommendations
Continuously learn and improve recommendation algorithms based on user interactions
Ensure recommendations are diverse to avoid creating echo chambers