Sequential Recommendation for Optimizing Both Immediate User Engagement and Long-term Retention

Core Concepts

The core message of this paper is to introduce DT4IER, a novel Decision Transformer-based framework that effectively balances the optimization of immediate user engagement and long-term user retention in sequential recommendation scenarios.

Abstract

The paper presents a novel framework, DT4IER, for sequential recommendation that aims to optimize both short-term user engagement and long-term user retention. The key highlights are: The authors emphasize the importance of balancing immediate user feedback (e.g., click-through rate) and long-term user retention (e.g., return frequency) in real-world recommendation scenarios. They propose an innovative multi-reward setting in the Decision Transformer framework, which adaptively balances short-term and long-term rewards based on user-specific features. This helps the model strike a better equilibrium between immediate engagement and sustained user retention. The authors introduce a high-dimensional encoder module to effectively capture the intricate relationships among different tasks, and a contrastive learning objective to ensure the predicted action embeddings for distinct rewards are well-separated. Extensive experiments on three real-world datasets demonstrate the superior performance of DT4IER compared to state-of-the-art Sequential Recommender Systems (SRSs) and Multi-Task Learning (MTL) models, in terms of both recommendation accuracy and user retention metrics. The ablation study highlights the importance of the key components in DT4IER, such as the adaptive RTG balancing, multi-reward embedding, and contrastive learning, in driving the model's overall effectiveness. The authors also provide insights into the impact of different RTG prompting proportions on the model's recommendation performance.

Stats

The paper presents the following key statistics and figures: "The learning objective of RL is to determine an optimal policy that maximizes the expected cumulative return E[∑T t=1 γtrt] given a specific reward function and discount rate." "The RTG can be expressed as: R̂t = [∑T i=t rs,i, ∑T i=t rl,i]" "The objective function can be expressed as: L = Lcross + αLcontra"

Quotes

"To circumvent these problems and unlock the full potential of RL-based recommendation systems, the innovative Decision Transformer (DT) has been introduced and then applied in RS." "We posit that focusing solely on immediate user feedback or long-term retention is insufficient. A more holistic approach requires optimizing both metrics simultaneously, offering a comprehensive perspective on user behavior." "Our innovative framework applies a novel multi-reward setting that balances immediate user responses with long-term retention signals by user-specific features, and then complements by a corresponding high-dimensional embedding module and a contrastive loss term."

Key Insights Distilled From

Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention

by Ziru Liu,Shu... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03637.pdf

Deeper Inquiries

How can the proposed DT4IER framework be extended to handle more than two reward signals, such as incorporating additional user engagement metrics beyond click-through rate and return frequency?

The DT4IER framework can be extended to handle more than two reward signals by incorporating additional user engagement metrics through a few key steps: Reward Design: Introduce new reward signals that capture different aspects of user engagement, such as dwell time, interaction depth, or social sharing. These rewards should be carefully designed to reflect the specific user behaviors or actions that are important for the platform. Adaptive RTG Balancing: Modify the adaptive RTG balancing module to accommodate the new reward signals. This may involve adjusting the user-specific features used for reweighting the RTG sequence to incorporate the new metrics effectively. Multi-reward Embedding: Enhance the multi-reward embedding module to handle the additional reward signals. This may involve creating new meta-embeddings specific to each new metric and incorporating them into the overall reward representation. Objective Function: Update the objective function to optimize for the new reward signals alongside the existing ones. This may require adjusting the loss functions and weighting schemes to ensure a balanced optimization across all user engagement metrics. By following these steps and carefully integrating new reward signals into the DT4IER framework, it can effectively handle multiple user engagement metrics beyond click-through rate and return frequency, providing a more comprehensive and personalized recommendation experience.

What are the potential limitations of the adaptive RTG balancing approach, and how could it be further improved to handle more complex user behavior patterns?

The adaptive RTG balancing approach, while effective, may have some limitations that could impact its performance in handling more complex user behavior patterns: Feature Selection: The effectiveness of the RTG balancing approach heavily relies on the selection and quality of user-specific features used for reweighting the RTG sequence. Inaccurate or insufficient features may lead to suboptimal balancing and performance. Complex Interactions: Handling more complex user behavior patterns may require a more sophisticated weighting mechanism that can capture intricate relationships between different reward signals. The current approach may struggle to adapt to highly dynamic and diverse user behaviors. Scalability: As the number of reward signals increases, the scalability of the adaptive RTG balancing approach may become a concern. Managing a large number of user engagement metrics and their corresponding weights could pose challenges in terms of computational complexity and model interpretability. To improve the adaptive RTG balancing approach for handling more complex user behavior patterns, the following strategies could be considered: Advanced Feature Engineering: Enhance the feature selection process by incorporating more advanced user-specific features that capture nuanced aspects of user behavior. This could involve leveraging advanced techniques such as deep learning for feature representation. Dynamic Weighting Mechanism: Develop a more dynamic and adaptive weighting mechanism that can adjust the importance of different reward signals based on the context and user interactions. This could involve incorporating reinforcement learning techniques for adaptive weighting. Hierarchical Balancing: Implement a hierarchical balancing approach that can handle multiple levels of reward signals, allowing for a more granular and nuanced representation of user engagement metrics. By addressing these limitations and implementing these improvements, the adaptive RTG balancing approach can be enhanced to effectively handle more complex user behavior patterns and provide more accurate and personalized recommendations.

Given the success of DT4IER in sequential recommendation, how could the core principles be applied to other domains, such as personalized content recommendation or product search, to achieve a similar balance between short-term and long-term objectives?

The core principles of the DT4IER framework can be applied to other domains, such as personalized content recommendation or product search, by adapting the following strategies: Reward Design: Define relevant reward signals specific to the domain, considering both short-term user engagement metrics (e.g., click-through rate, dwell time) and long-term objectives (e.g., user retention, conversion rate). These rewards should align with the goals of the platform and the desired user behaviors. Adaptive RTG Balancing: Customize the adaptive RTG balancing module to suit the characteristics of the new domain. This may involve incorporating domain-specific user features for reweighting the RTG sequence and optimizing the balance between short-term and long-term rewards. Multi-reward Embedding: Extend the multi-reward embedding module to accommodate the new domain's reward signals. Create meta-embeddings tailored to the specific metrics of personalized content recommendation or product search to capture the nuances of user preferences and interactions. Objective Function: Tailor the objective function to optimize for the unique reward signals and objectives of the domain. Adjust the loss functions and weighting schemes to achieve a balanced optimization strategy that considers both short-term performance and long-term goals. By applying these core principles to other domains, the DT4IER framework can effectively achieve a similar balance between short-term and long-term objectives in personalized content recommendation or product search. This approach can lead to more accurate and relevant recommendations, ultimately enhancing user satisfaction and engagement in diverse application scenarios.