Enhancing Real-Time Streaming Recommendation Systems with Time-Aware User Preferences

Conceptos Básicos

Enabling recommendation systems to effectively model users' dynamic preferences over time to provide personalized and timely recommendations.

Resumen

The paper proposes an effective method called "Interest Clock" to enable recommendation systems to perceive time information in real-time streaming environments. The key idea is to encode users' time-aware preferences into a "clock" (hour-level personalized features) and then use Gaussian distribution to smooth and aggregate them into the final interest clock embedding according to the current time for the final prediction.
The paper first extracts time-aware personalized features by computing users' preferences from their consumption data in each hour of the past 30 days. It then introduces three methods to aggregate the hour-level features: Naive Clock, Adaptive Clock, and Gaussian Interest Clock. The Gaussian Interest Clock approach, which uses empirical Gaussian weights to combine the 24-hour features, is found to be the most effective in addressing the issues of sudden changes and overfitting to the current time faced by the other two methods.
The proposed Interest Clock method is evaluated through both online A/B testing and offline experiments on large-scale industrial datasets. The online A/B testing results show that Interest Clock can bring significant improvements of +0.509% and +0.758% on user active days and app duration, respectively, compared to the baseline model. The offline experiments also demonstrate the effectiveness of Interest Clock across different datasets and metrics.
Furthermore, the paper provides an insightful analysis on the influence of time information in real-world recommendation systems, revealing that the distribution of content provided by recommendation systems varies significantly over the course of a day, which highlights the importance of modeling users' dynamic preferences.

Estadísticas

User preferences follow a dynamic pattern over a day, e.g., at 8 am, a user might prefer to read news, while at 8 pm, they might prefer to watch movies.
The online A/B testing results show that Interest Clock can bring significant improvements of +0.509% and +0.758% on user active days and app duration, respectively, compared to the baseline model.

Citas

"Time modeling aims to enable recommendation systems to perceive time changes to capture users' dynamic preferences over time, which is an important and challenging problem in recommendation systems."
"To enable recommendation systems to perceive time changes, we propose an effective method Interest Clock."
"Interest Clock first encodes users' time-aware preferences into a clock (hour-level personalized features) and then uses Gaussian distribution to smooth and aggregate them into the final interest clock embedding according to the current time for the final prediction."

Ideas clave extraídas de

Interest Clock: Time Perception in Real-Time Streaming Recommendation System

by Yongchun Zhu... a las arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19357.pdf

Consultas más profundas

How can the proposed Interest Clock method be extended to capture users' preferences at an even finer time granularity, such as minute-level or second-level?

To extend the Interest Clock method to capture users' preferences at a finer time granularity, such as minute-level or second-level, several adjustments can be made to the existing framework. One approach could involve breaking down the hour-level features into smaller time intervals, such as minutes or seconds, and calculating user preferences within those intervals. This would require a more granular data collection process to capture user interactions at a more detailed level. Additionally, the Gaussian distribution smoothing technique used in the Interest Clock method could be adapted to handle the increased number of time intervals, ensuring a smooth transition between preferences at different time points. By incorporating minute or second-level preferences, the model would be able to provide more precise and timely recommendations tailored to users' preferences at a more granular level.

What other types of dynamic user preferences, beyond time-of-day, could be modeled to further improve the personalization and timeliness of recommendations?

Beyond time-of-day preferences, other dynamic user preferences that could be modeled to enhance personalization and timeliness of recommendations include:

Seasonal Preferences: Users may have varying preferences based on seasons, holidays, or special events. Modeling seasonal preferences can help recommend relevant content during specific times of the year.

Weather-based Preferences: User preferences may change based on weather conditions. For example, users might prefer different types of content on rainy days compared to sunny days. Incorporating weather data into the recommendation system can improve the relevance of recommendations.

Contextual Preferences: Preferences can also be influenced by the user's current context, such as location, device type, or social interactions. By considering contextual factors, the recommendation system can deliver more personalized and timely recommendations based on the user's situation.

User Engagement Patterns: Understanding how users engage with the platform over time can provide valuable insights into their preferences. By modeling user engagement patterns, the system can adapt recommendations to align with the user's behavior and interests.

By incorporating these additional types of dynamic user preferences into the recommendation system, it can offer more personalized and timely recommendations that cater to the user's evolving needs and preferences.

How can the insights gained from the analysis of time-varying content distribution be leveraged to develop more proactive and anticipatory recommendation strategies?

The insights gained from the analysis of time-varying content distribution can be leveraged to develop more proactive and anticipatory recommendation strategies in the following ways:

Temporal Trend Analysis: By analyzing the patterns of content consumption over time, the recommendation system can identify trends and seasonality in user preferences. This information can be used to anticipate shifts in user behavior and adjust recommendations accordingly.

Real-time Personalization: Leveraging real-time data on time-varying content distribution, the recommendation system can dynamically adjust recommendations based on current trends and user interactions. This real-time personalization can enhance the relevance and timeliness of recommendations.

Event-based Recommendations: Insights from time-varying content distribution can help identify key events or moments that impact user preferences. By proactively recommending content related to upcoming events or trends, the system can anticipate user needs and provide relevant recommendations ahead of time.

Dynamic Content Scheduling: Understanding how content popularity changes over time can inform the scheduling of content recommendations. By aligning recommendations with peak engagement periods, the system can maximize user interaction and satisfaction.

Overall, by leveraging insights from the analysis of time-varying content distribution, the recommendation system can proactively anticipate user preferences, deliver timely recommendations, and enhance the overall user experience.