Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks (1706.04148v5)

Abstract: Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs.

• The paper demonstrates that a hierarchical GRU structure significantly enhances personalization by capturing both immediate session interactions and long-term user preferences.
• The proposed model combines dual-level GRUs with a user-parallel mini-batch approach, offering scalability and efficient handling of diverse user histories.
• Evaluations on XING and video-streaming datasets show that HRNNs can improve ranking metrics by 15-30% compared to traditional session-based and collaborative filtering methods.

Overview of Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

The paper presents an advanced approach to enhancing session-based recommendations through the integration of Hierarchical Recurrent Neural Networks (HRNNs). This research addresses the significant challenge of incorporating user personalization in session-based recommendation systems, where traditional recommendation techniques are typically applied without leveraging long-term user-specific data.

Proposed Model and Methodology

The authors introduce a hierarchical model that utilizes multiple levels of Gated Recurrent Units (GRUs) to capture both within-session dynamics and the evolution of user preferences across sessions. This architecture consists of a lower-level GRU, handling the immediate session interactions, and a higher-level GRU, modeling the user’s activity over multiple sessions. The model is trained using end-to-end backpropagation and employs a user-parallel mini-batch training mechanism to streamline processing diverse user histories efficiently.

Experimental Evaluation

The HRNN model was evaluated on two industrial datasets: one from XING, a job-posting platform, and another proprietary dataset from a video streaming site. Both datasets were preprocessed to remove low-frequency interactions and construct coherent user sessions. The results demonstrate a marked improvement of 15% to 30% in ranking metrics over state-of-the-art session-based RNNs and item-based collaborative filtering techniques. Particularly, the HRNN Init variant, which uses historical user data to initialize session-level states, showed superior performance without enforcing context across every session-level prediction.

Key Findings

1. Personalization Advantage: HRNNs exhibit a clear advantage in environments where user interactions span multiple sessions with identifiable user profiles. The hierarchical structure seamlessly carries latent user preferences forward, leading to more personalized and accurate recommendations.
2. Scalability and Flexibility: Although computationally more complex than traditional RNNs, HRNNs scale well across varying session lengths and user histories. The user-parallel mini-batch mechanism improves computational efficiency without sacrificing performance.
3. Domain-specific Insights: The disparity in HRNN performance across datasets indicates that the model's effectiveness can be domain-dependent, particularly when user interests are either strongly session-based or more persistent cross-session.

Implications and Future Directions

This work has substantial implications for the development of recommendation systems that require balancing between session-specific rapid dynamics and long-term user interests. The HRNN approach offers a viable framework for enhancing personalization in sectors like e-commerce and digital media, where identifying recurring users can enable more tailored experiences.

Future research could explore integrating attention mechanisms to augment the user-level GRU’s capability in selectively focusing on relevant historical interactions. Additionally, incorporating rich item and user features could further refine the model's personalization prowess. As HRNNs continue to evolve, their application could extend beyond standard recommendation systems, potentially impacting areas such as personalized learning environments and tailored content delivery platforms. Overall, the hierarchical approach proposed in this paper paves the way for more nuanced and effective recommendation methodologies.