What are some best practices for updating user profiles for recommender systems over time?

1 Why update user profiles?

User profiles are not only a snapshot of the user's current state, but also a history of the user's interactions with the recommender system. Updating user profiles over time allows the system to learn from the user's behavior, feedback, and context, and to adapt the recommendations accordingly. Updating user profiles can also help to avoid the problems of cold start, where the system has insufficient information about new or inactive users, and concept drift, where the user's preferences change over time due to external factors.

2 How to update user profiles?

Different methods and techniques can be used to update user profiles, depending on the type and source of data, the frequency and granularity of updates, and the goal and design of the recommender system. Explicit feedback is when users provide direct ratings, reviews, or preferences for the items or services they interact with. Implicit feedback is when the system infers user preferences from their behavior, such as clicks, views, purchases, or dwell time. Contextual data involves collecting and analyzing user contextual information, such as location, time, device, or mood. Social data leverages the user's social network to update their profile with social influences, trust, or similarity.

3 When to update user profiles?

When updating user profiles, several factors must be taken into consideration, such as the availability and reliability of data, the stability and volatility of user preferences, and the trade-off between accuracy and efficiency. Batch updates are suitable for large-scale or offline recommender systems with sparse or noisy data and slow-changing user preferences, while incremental updates are better for dynamic or online recommender systems with rich or relevant data and rapidly changing user preferences. For complex or adaptive recommender systems, hybrid updates combining batch and incremental updates may be the best approach, with batch updates for long-term or global user profiles, and incremental updates for short-term or local user profiles.

4 What to update in user profiles?

The content and structure of user profiles vary depending on the model and algorithm of the recommender system, as well as the domain and objective of the application. Features like demographics, preferences, or personality can be added, deleted, or modified based on new data or feedback. Weights indicate the importance or relevance of each feature for the user's preferences or goals and can be increased, decreased, or normalized depending on new data or feedback. Clusters classify user profiles based on similarity or dissimilarity and can be assigned, reassigned, or merged based on new data or feedback. Rules define user preferences or constraints for items or services and can be created, updated, or deleted depending on new data or feedback.

5 How to evaluate user profile updates?

Measuring and evaluating the effectiveness and impact of updating user profiles can be done through various metrics and methods, depending on the purpose and performance of the recommender system. Accuracy, which can be measured by comparing updated user profiles with the ground truth or baseline user profiles using mean absolute error, root mean squared error, or precision and recall, is one common metric. Diversity can also be assessed by analyzing the distribution or entropy of updated user profiles using Gini index, Shannon index, or coverage and novelty. Lastly, stability can be determined by tracking changes or fluctuations of updated user profiles over time using temporal correlation, temporal variance, or retention and churn metrics.