2026 Roadmap for Recommendation Systems: Challenges, Research Directions, and OneRec Integration

The conventional recommendation paradigm—recall + ranking + re‑ranking on the model side and sample mining + feature engineering + online scoring on the system side—has reached its limits, leaving user satisfaction far from ideal. A new definition of a "good" recommender is needed, prompting a series of research topics for the next three years.

1. Retention Improvement : Analyzes factors influencing DAU and retention, such as hot‑show content, causal inference, multi‑objective sub‑goals, and session‑level value modeling (e.g., 30‑minute engagement).

2. User Growth : Discusses user segmentation, high‑value actions, uplift modeling, and the integration of external data to compensate for sparse user signals.

3. Content Ecosystem : Defines ecosystem health, proposes metrics for content diversity, plan‑economy controls (PID), and creator lifecycle management to align supply with user demand.

4. Multi‑Objective Pareto Optimality : Explores optimizing multiple KPIs (clicks, orders, likes, etc.) simultaneously, handling trade‑offs via Pareto front analysis and hyper‑parameter search.

5. Time‑Long‑Term Value Estimation : Shifts focus from short‑term clicks to session‑level value using Markov Decision Processes and reinforcement learning, citing works from Microsoft and Tencent Video.

6. Space‑Wide Whole‑Site Optimization : Emphasizes joint modeling of multiple app scenes (home, recommendation, cart) and user journey analysis to avoid counter‑productive local optimizations.

7. Interactive Recommendation Systems (IRS) : Covers implicit and explicit dialogue‑based recommenders, intent recognition, and knowledge‑graph‑enhanced list recommendations.

8. Thousand‑Model Personalization : Investigates per‑user model construction, resource‑aware multi‑task and meta‑learning approaches, and feature‑gate fusion for ultra‑personalized CTR prediction.

9. Recommendation as Decision Problem : Frames recommendation as a stochastic decision process, highlighting uncertainty, multi‑criteria (diversity, fatigue, timeliness), and MDP‑based solutions.

10. OneRec – Multi‑Source Fusion Framework : Introduces the open‑source OneRec library that integrates behavior, content, social, and knowledge‑graph signals; provides code repository and outlines ongoing 2024‑2025 research directions (social‑behavior fusion, search‑recommendation fusion, multimodal fusion, cross‑scenario fusion).

The article concludes with a reference list of recent papers and a Q&A session addressing practical details such as session length thresholds, PID control in content planning, and differences between large language models and traditional recommender models.