Enhancing Email Recommendations with MOSR Algorithm

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Aug 21 2023

In an article recently submitted to the ArXiv* server, researchers introduced MOSR (Multi-Objective Stationary Recommender), a pioneering online algorithm aimed at addressing the challenge of generating personalized email rankings based on user preferences that evolve over time.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.

By dynamically balancing criteria like sender/topic relevance, email recency, and conciseness, MOSR adapts to changing user preferences and achieves superior performance compared to baseline methods, particularly when preferences are non-stationary. Tested on the Enron Email Dataset, MOSR's effectiveness in maintaining stable rankings across diverse email samples underscores its robustness, offering a significant step towards enhancing multi-objective email re-ranking systems for improved user satisfaction.

Related work

Current strategies in email re-ranking or recommendation have predominantly emphasized the enhancement of relevance or priority, often relying on specific attributes. For instance, certain methodologies incorporate characteristics of the sender-receiver relationship, some leverage topic models and others blend text similarity with temporal attributes. However, these approaches have limitations in accuracy, and adaptability and overlook additional factors beyond relevance, such as novelty or diversity. Moreover, they often do not explicitly consider changes in preferences over time. Recent studies have begun to explore objectives beyond mere relevance in recommendation systems, aiming to optimize factors that influence user engagement rather than solely focusing on item relevance.

Proposed method

The MOSR algorithm involves several steps to enhance email recommendations. It combines preferences through ordered weight averaging (OWA) and progresses through candidate identification, ranking, loss computation, and weight updates using Model Reference Adaptive Control (MRAC). This dynamic approach balances criteria like closeness, timeliness, and conciseness. It uses multiple scores to adapt to evolving email history, and the MRAC model refines user preferences by comparing true and predicted rankings, resulting in personalized and evolving email recommendations.

The goal is to comprehend email importance based on factors such as closeness, timeliness, and conciseness. These criteria are defined and quantified for email re-ranking. The insider space, flow lists, and job levels form the basis of an online re-ranking approach. The problem is framed as a unique recommendation challenge involving metrics like interpersonal closeness, timeliness urgency, and information conciseness. Heatmaps illustrate evolving user behaviors, emphasizing the requirement for adaptability. Thus, an MRAC-based symmetric aggregation method is introduced to address dynamic user preferences and shifting conditions.

Experimental results

The study employs the Enron email dataset, encompassing 500K emails exchanged among 1K Enron Corporation employees. The dataset's focus lies in emails sent between 1999 and 2002. The dataset is divided into EnronA and EnronB segments, simulating a real-world email recommendation system's time flow. This division is based on the Enron scandal's occurrence in October 2001. The evaluation of various ranking methods, including Logistic Regression, AdaBoost, OWA-based rankers, and the proposed MOSR method, reveals that MOSR excels in accommodating evolving user preferences during email re-ranking. The analysis further demonstrates MOSR's robustness against downsampling and adeptness at balancing stability and adaptability.

In terms of technical intricacies, parameter-tuning decisions, and complexities are explored. These analyses underline MOSR's efficiency and effectiveness in the email re-ranking process. The research emphasizes the method's capability to adapt to fluctuating user preferences and robust performance in the face of external factors. Overall, the study underscores MOSR as a potent approach for addressing the challenge of email re-ranking and adapting to evolving user needs.

Contribution of this paper

The key contributions of this study can be summarized as follows:

• Problem Framing: Introducing a novel perspective characterizes email re-ranking as a multi-objective online recommendation challenge, aiming to optimize user satisfaction across factors like closeness, timeliness, and conciseness. These dimensions exhibit dynamic variations across users and over time.
• Adaptive Control Model: MOSR, an innovative adaptive control model, learns a reference vector from past data and adjusts it in response to current feedback. This vector signifies the significance of each criterion for individual users at any given moment. Leveraging reinforcement learning techniques, MOSR adapts dynamically without necessitating re-training or compromising user privacy.
• Empirical Evaluation: A comprehensive evaluation of MOSR using the Enron Email Dataset highlights its superiority over various baseline methods, as measured by improved ranking quality through Normalized Discounted Cumulative Gain (NDCG). Significantly, MOSR adeptly manages non-stationary user preferences, offering consistent recommendations despite evolving user criteria. Furthermore, robustness tests on smaller datasets with high variance reaffirm MOSR's consistent outperformance of alternative approaches.

Conclusion

To summarize, this paper addressed the challenge of email re-ranking by introducing MOSR, an algorithm that tackles the complexities of balancing three distinct criteria: closeness, timeliness, and conciseness. The study highlighted the limitations of simplistic weighted averaging and proposed MRAC to adapt dynamically to changing user preferences.

Evaluation of the Enron Email Dataset showcased MOSR's superior performance over other benchmarks, particularly in accommodating shifting preferences. The algorithm's resilience to email variations and avoidance of content analysis for privacy concerns add to its merits, contributing an innovative approach to email re-ranking that aligns with multiple user objectives and evolves with user needs.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.