Future Directions of Recommendation Systems: Retention, User Growth, Content Ecosystem, Multi‑Objective Optimization, and Large‑Model Fusion

Overview – Conventional recommendation pipelines (recall + ranking + re‑ranking, sample mining + feature engineering + online scoring) have reached their limits, and user satisfaction remains far from ideal. A new definition of a "good" recommender is needed, prompting research and industry to explore fresh paradigms.

Agenda – The talk covers ten topics: 1) Retention improvement, 2) User growth, 3) Content ecosystem, 4) Multi‑objective Pareto optimality, 5) Time‑long‑term value estimation, 6) Site‑wide spatial optimization, 7) Interactive Recommendation Systems (IRS), 8) Personalized per‑user models ("thousand‑person‑thousand‑model"), 9) Treating recommendation as a decision‑making problem, 10) OneRec – a multi‑source large‑model‑based recommender.

1. Retention Improvement – Retention is the lifeblood of any app; three main research directions are identified: (a) causal analysis of factors such as hot‑show popularity, (b) decomposing retention into sub‑goals and re‑ranking, and (c) modeling multi‑day user value (e.g., 48‑hour consumption) rather than a single‑day click.

2. User Growth – Defines growth as converting inactive users into active ones. Topics include layered user segmentation with high‑value actions, uplift modeling of marketing incentives, and cross‑source knowledge fusion to compensate for sparse user data.

3. Content Ecosystem – Describes the supply‑side health of a platform as an ecosystem that must reflect user demand and possess self‑regulating mechanisms. Discusses metrics, plan‑economy control (PID), and creator‑level growth management.

4. Multi‑Objective Pareto Optimality – Aims to simultaneously improve click, order, interaction, etc. Highlights the difficulty of conflicting objectives and the need to locate Pareto frontiers for trade‑offs.

5. Time‑Long‑Term Value Estimation – Moves beyond instant value to model session‑level value (e.g., 30‑minute user engagement) using Markov decision processes and reinforcement learning, citing works from Microsoft and Tencent.

6. Site‑Wide Spatial Optimization – Emphasizes joint optimization across multiple app scenes (home feed, similar‑item, cart) by analyzing typical user journeys and applying multi‑scenario reinforcement learning.

7. Interactive Recommendation Systems (IRS) – Explores implicit and explicit dialogue‑based recommenders, combining large‑language‑model (LLM) capabilities with traditional ranking and intent detection.

8. Personalized Per‑User Modeling ("Thousand‑Person‑Thousand‑Model") – Discusses the feasibility of building a dedicated model per user, challenges of resource consumption, and possible solutions via multi‑task or meta‑learning.

9. Recommendation as a Decision Problem – Frames recommendation and user feedback as stochastic decision processes (MDP), highlighting uncertainty, multi‑round interaction, and factors beyond pure interest matching (diversity, fatigue, timeliness).

10. OneRec – Large‑Model Fusion – Introduces the open‑source OneRec library that integrates multi‑source signals (behavior, content, social graph, knowledge graph) via plug‑and‑play modules. Provides a roadmap for deeper semantic understanding using LLMs and outlines future work on interaction, deep semantics, knowledge expansion, and generative capabilities.

References – Lists recent papers on long‑term engagement modeling, multi‑task recommendation, multi‑source enhancement, Pareto‑efficient algorithms, reinforcement‑learning news recommendation, multi‑scenario ranking, and calibration‑based meta‑rec.