ICDM 2025 Workshop on Multimodal Search and Recommendations (MMSR 2025)
Overview
| Field | Content | |------|------| | Title | ICDM MMSR 2025 | | Organizer | See official website | | Link | https://icdm-mmsr.github.io/ | | Resource type | Conference / Workshop | | Section | Conferences, Workshops |
MMSR 2025 is a workshop co-located with ICDM 2025 (IEEE International Conference on Data Mining), focusing on multimodal search and recommendations — a research direction at the intersection of information retrieval and large-scale search/recommender systems.
Background and Scope
In large-scale search, recommendation, and personalization systems, information retrieval has long faced challenges in efficiency, scalability, and user intent understanding. Traditional pipeline approaches tend to treat retrieval, ranking, and generation as separate stages, which makes it hard to meet LLM-era demands for natural language interaction, multi-hop reasoning, and real-time knowledge. MMSR 2025 was proposed in this context, aiming to survey and advance the theory and practice of this cross-disciplinary area.
Core scenarios include:
- Open-domain information access
- Enterprise knowledge retrieval
- Conversational search
- Semantic understanding in recommender systems
- End-to-end architectures combining external knowledge sources with generative models
- A unified perspective that organizes scattered related work into a comparable framework.
- Clear decomposition of method components: representation learning, retrievers, rerankers, planners, generators, and feedback mechanisms.
- Reproducible benchmarks, datasets, and taxonomy tables that lower the entry cost for new researchers.
- Interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration, and paths from research prototypes to industrial systems.
- Open problems: evaluation trustworthiness, latency and cost, hallucination and safety, cross-lingual and multimodal scaling.
- 2025 SIGIR Workshop on eCommerce
- Activate Activate
- CIKM 2024 1st Workshop on Multimodal Search and Recommendations
- EACL 2024 Workshop on Personalization of Generative AI Systems
- Haystack Haystack
- KDD 2024 Workshop on Generative AI for Recommender Systems
- Source entry: ICDM MMSR 2025. Official site: https://icdm-mmsr.github.io/
Key Topics and Contributions
Workshops in this area typically cover:
Typical Methodological Pipeline
1. Input and representation: encode queries, documents, and user context as dense or sparse representations, or structured prompts. 2. Core modules: retrievers, rerankers, planners, memory modules, and tool interfaces, chained or combined by task. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and bootstrapped data synthesis. 4. Inference strategies: single-pass retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.
Insights for Search / Rec / Personalization
1. Architecture: cascaded retrieval + reranking + generation remains mainstream, but agentic paradigms are turning "retrieval frequency and strategy" itself into a learnable object. 2. Data: high-quality instruction data and click/session logs are both critical; synthetic data must guard against knowledge leakage and distribution shift. 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge needs cross-validation with human evaluation. 4. Product: latency, cost, explainability, and safety policies are hard constraints for industrial deployment — one cannot optimize academic benchmarks alone.
Limitations and Notes
Common limitations in this research area include: experiment scale constrained by GPU budgets, mismatch between benchmarks and real user distributions, unknown cross-lingual generalization due to English-centric data, and safety risks of agent systems operating on the open web. Future directions include more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal/fairness constraints for recommendation.
Cross-references
Glossary
| Term | Meaning | |------|------| | IR | Information Retrieval | | RAG | Retrieval-Augmented Generation | | LTR | Learning to Rank | | nDCG | Normalized Discounted Cumulative Gain, a ranking quality metric | | Agentic Search | Modeling search as sequential decision-making and tool calling by an agent | | Gen-IR | Generative Information Retrieval |