Hi, I'm Wei-Wei Du.

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Applied Research Scientist at Sony specializing in recommender systems, off-policy evaluation, and LLM applications.

About

I am an Applied Research Scientist at Sony, working on personalization, recommender systems, and off-policy evaluation to enhance user engagement. Before joining Sony, I worked as a Machine Learning Scientist Intern at Appier, developing real-time bidding models. Before graduation, my research spanned several domains, including property valuation (self-supervised learning for few-shot scenarios, graph neural networks), natural language processing (depression detection, multi-modal fact-checking), and Sports AI, with multiple publications on these topics. In 2024, I published a comprehensive survey on self-supervised learning to make a broader impact on the academic community.

With over five years of experience in the field of Machine Learning, I am passionate about making an impact by applying data-driven solutions to real-world applications and continuously seeking new knowledge. In my free time, I enjoy photography, cycling, and practicing yoga.

Programming: Python, Linux, Shell, R, SQL, PySpark, Java, C++
Languages: English, Mandarin
Tools & Technologies: Git, Docker, AWS, GCP

Experience

Sony

Applied Research Scientist

Develop off-policy evaluation pipelines to assess and improve recommender system performance, reducing the cost and risk of online A/B testing for a streaming platform with 30M+ monthly active users.
Lead research on LLM-based recommender systems for next-generation fan engagement. (first-author paper accepted at RecSys 2025)
Collaborate with global R&D teams to deliver solutions across movies, gaming, music, and e-commerce.

Oct 2023 – Present | Tokyo, Japan

Appier

Machine Learning Scientist Intern

Implemented an ensemble real-time bidding model with new data-driven features from 10M+ e-commerce click stream data that achieved 2x performance in production.
Conducted tree analysis and feature importance with SHAP to analyze model behavior.
Cooperated with 3 data scientists to build the RTB model for re-engagement campaigns.

Jun 2022 - Nov 2022 | Taipei, Taiwan

Research Papers

                
 Off-Policy Evaluation for Ranking
                  Off-Policy Evaluation for Ranking Policies under Deterministic Logging Policies (ICLR-26)
                
Accomplishments
                    Address the critical challenge of off-policy evaluation for ranking systems when the logging policy is deterministic, a common yet under-explored scenario in production systems.
                  
                    Propose a principled estimator that enables unbiased evaluation of ranking policies without requiring stochastic exploration in data collection.
                  
 LLM-based Recommender
                  Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation (RecSys-25)
                
Accomplishments
                    The first work to integrate time intervals into LLMs for sequential recommendation.
                  
                    Propose Interval-Infused Attention to capture the temporal relevance between items and intervals.
                  
                    Introduce a novel perspective on the cold-start problem by considering time intervals and reveal that existing methods suffer from significant performance drops in interval cold-start scenarios.
                  
 SSL for NSTD
                  A Survey on Self-Supervised Learning for Non-Sequential Tabular Data (ACML-24)
                
Accomplishments
                    The first comprehensive survey of recent advancements in SSL4NS-TD, consisting of problem definitions, taxonomy, application issues, NS-TD datasets, and evaluation protocols
                  
                    Evaluate representative SSL4NS-TD approaches of each learning category on the most recent and large-scale benchmark, TabZilla.
                  
                    Highlight the addressed challenges and future directions in SSL for the existing NS-TD methods.
                  
AI for Sport
                  Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset (IJCAI-24 Demo)
                
Accomplishments
                    Introduce ShuttleSet22, a stroke-level badminton singles dataset collected from real-world high-ranking matches in 2022
                  
                    Initiated a challenge within CoachAI Badminton Challenge 2023 (https://sites.google.com/view/coachai-challenge-2023/) in conjunction with IJCAI 2023.
                  
Depression Detection
                  Ensemble Models with VADER and Contrastive Learning for Detecting Signs of Depression from Social Media (ACL-22 Workshop)
                
Accomplishments
                    Developed an ensemble model with VADER and contrastive learning for detecting depression.
                  
                    Won second place in 30+ teams without any auxiliary information.
                  
Multi-Modal Fact Checking
                  Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact Verification (AAAI-23 Workshop)
                
Accomplishments
                    Introduced a parameter-efficient large foundation model by utilizing adapters and additional features.
                  
                    Incorporated co-attention modules for different modalities (image and text) and different types (claim and
                    document).
                  
                    Surpassed 25.9% compared with the official baseline.
                  
SSL for Few-Shot Learning
                  Dora: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal (CIKM-23)
                
AccomplishmentsThe first work focusing on low-resource real estate appraisal, which meets the needs of real-world scenarios.
Introduced with novel and effective intra- and inter-sample SSL objectives to learn robust geographical knowledge from unlabeled records.
Illustrate a developed system of DoRA and the real-world industrial scenarios for cities and towns with extremely limited transactions.

Graph-Based Learning
                  Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal (PAKDD-24)
                
AccomplishmentsIncorporate the relationship between the target transaction and neighbors with an attention mechanism
Utilize the neighbors’ price information to predict a preliminary value
Introduce dynamic predictor to model the price of target transactions with different characteristics

Personal Projects - Agentic Development

Where To Go

LLM-Powered Travel Destination Recommender

Highlights

End-to-end retrieval-and-ranking pipeline modeled on industrial recommender systems: LLM-based query understanding with schema-constrained extraction, candidate filtering, embedding retrieval, and LLM listwise reranking with explanations grounded in user intent.
Reduced latency by caching hash-invalidated embedding indexes and prefetching external API enrichment concurrently with LLM reranking.

Home screen of the First and Last multiplayer word game

First & Last

Real-Time Multiplayer Word Game

Highlights

Server-authoritative architecture with a phase state machine and server-driven timers, synchronizing clients via personalized WebSocket state snapshots.
External API validation with offline fallback for resilient real-time gameplay.

Invited Talks

2025/09 RecSys 2025 Workshop on Evaluating and Applying Recommender Systems with LLMs - LLMs for Next-Generation Recommender Systems: From Understanding User Behavior to Deployment [link]