Leveraging User Behavior History for Personalized Email Search (2102.07279v2)

Abstract: An effective email search engine can facilitate users' search tasks and improve their communication efficiency. Users could have varied preferences on various ranking signals of an email, such as relevance and recency based on their tasks at hand and even their jobs. Thus a uniform matching pattern is not optimal for all users. Instead, an effective email ranker should conduct personalized ranking by taking users' characteristics into account. Existing studies have explored user characteristics from various angles to make email search results personalized. However, little attention has been given to users' search history for characterizing users. Although users' historical behaviors have been shown to be beneficial as context in Web search, their effect in email search has not been studied and remains unknown. Given these observations, we propose to leverage user search history as query context to characterize users and build a context-aware ranking model for email search. In contrast to previous context-dependent ranking techniques that are based on raw texts, we use ranking features in the search history. This frees us from potential privacy leakage while giving a better generalization power to unseen users. Accordingly, we propose a context-dependent neural ranking model (CNRM) that encodes the ranking features in users' search history as query context and show that it can significantly outperform the baseline neural model without using the context. We also investigate the benefit of the query context vectors obtained from CNRM on the state-of-the-art learning-to-rank model LambdaMart by clustering the vectors and incorporating the cluster information. Experimental results show that significantly better results can be achieved on LambdaMart as well, indicating that the query clusters can characterize different users and effectively turn the ranking model personalized.

• The paper introduces CNRM, a context-dependent neural ranking model that integrates numerical user behavior features for enhanced personalized email search.
• It employs ranking features instead of raw text to effectively capture user context while mitigating privacy risks.
• Empirical results show significant NDCG improvements, demonstrating the robust integration of context vectors with models like LambdaMart.

Leveraging User Behavior History for Personalized Email Search

The research paper titled "Leveraging User Behavior History for Personalized Email Search" by Keping Bi et al. explores the challenges and innovations in personalized email search through the utilization of user behavior history. The paper proposes the Context-dependent Neural Ranking Model (CNRM) which integrates user search history to optimize the ranking performance in email search tasks.

Summary of Contributions

The central contribution of the paper lies in the novel method of utilizing user behavior history to enhance email search relevance and personalization. It presents a method distinct from prior art by focusing on numerical ranking features instead of raw textual data, addressing privacy concerns prevalent in similar research in web search.

1. Context-Dependent Neural Ranking Model (CNRM): The paper introduces CNRM that leverages the historical search behavior of users as context to improve personalization. This model contrasts with traditional methods that largely ignore contextual signals or rely on direct semantic matching of text.
2. Utilization of Numerical Features: Instead of using raw textual history, which could breach privacy, the authors employ ranking features that do not directly expose user content. This is crucial given the privacy-sensitive nature of email data.
3. Integration with LambdaMart: The paper explores how the query context information learned through CNRM can be incorporated into LambdaMart—a gradient boosting model—by clustering context vectors. This showcases the versatility of the approach across different ranking paradigms.
4. Significant Empirical Results: The model demonstrates noticeable improvements over baseline methods in personalization, with significant enhancements in NDCG scores. This confirms the efficacy of using query context derived from user history in both CNRM and its integration into LambdaMart.

Implications and Future Prospects

The implications of this work are multi-faceted. Practically, it suggests a pathway to more effective personalized search systems that respect user privacy. Theoretically, it contributes to our understanding of how user interaction history can be quantitatively leveraged in information retrieval systems.

The research indicates several avenues for future exploration. The integration of context-aware models with other non-linear ranking algorithms could be further refined. Additionally, end-to-end deep learning approaches that automatically determine the most relevant aspects of historical context for ranking without manual feature engineering could be explored. Furthermore, as AI techniques evolve, incorporating dynamic user modeling for continually updating user context definitions based on behavioral changes presents an intriguing direction.

This paper sets a robust foundation for advancing both the technical methodologies and practical applications of personalized email search. By balancing user privacy with enhanced personalization, this research provides a critical stepping stone for creating effective and secure information retrieval systems in increasingly data-sensitive environments.