CIKM 2024 1st Workshop on Multimodal Search and Recommendations
Overview
- Title: CIKM 2024 1st Workshop on Multimodal Search and Recommendations
- Link: https://cikm-mmsr.github.io/
- Type: Conference Workshop (co-located with CIKM 2024)
- Category: Conferences, Workshops
- A unified perspective that brings together previously scattered work into a comparable framework.
- A clear decomposition of method components: representation learning, retrievers, rerankers, planners, generators, and feedback mechanisms.
- Reproducible benchmarks, datasets, or taxonomies that lower the entry cost for follow-up research.
- Discussion of interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration, including paths from research prototypes to industrial systems.
- Open problems: evaluation trustworthiness, latency and cost, hallucination and safety, and cross-lingual / multimodal extension.
- 2025 SIGIR Workshop on eCommerce
- EACL 2024 Workshop on Personalization of Generative AI Systems
- ICDM MMSR 2025
- KDD 2024 Workshop on Generative AI for Recommender Systems
- Source: CIKM 2024 1st Workshop on Multimodal Search and Recommendations. See official site: https://cikm-mmsr.github.io/
Background and Scope
Large-scale search, recommendation, and personalization systems have long faced challenges around efficiency, scalability, and user intent understanding. Traditional pipeline-based approaches tend to treat retrieval, ranking, and generation as disconnected stages, which makes it difficult to meet the demands of the LLM era: natural language interaction, multi-hop reasoning, and up-to-date knowledge. The CIKM 2024 1st Workshop on Multimodal Search and Recommendations was organized against this backdrop, aiming to advance the theory and practice of multimodal search and recommendation.
Core application scenarios include open-domain information access, enterprise knowledge retrieval, conversational search, semantic understanding in recommender systems, and end-to-end architectures that combine external knowledge sources with generative models.
Core Topics and Contributions
Workshops in this space typically feature:
Typical Technical Approach
Methods in this area usually follow a four-step pattern: problem formulation → model/system design → training or construction → inference pipeline.
1. Input and representation: encode queries, documents, and user context as dense or sparse representations, or build structured prompts. 2. Core modules: retrievers, rerankers, planners, memory modules, and tool interfaces, chained or combined per task. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and bootstrapped data synthesis. 4. Inference strategies: single-round retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.
Key Takeaways for Search / Rec / Personalization
1. Architecture: cascaded retrieve-rerank-generate remains mainstream, but agentic paradigms are making retrieval frequency and strategy themselves learnable. 2. Data: high-quality instruction data matters as much as click/session logs; 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 should be cross-validated with human assessment. 4. Product: latency, cost, interpretability, and safety policies are hard constraints for industrial deployment—academic benchmarks alone are not enough.
Limitations and Notes
Typical limitations in this research area include experiment scale constrained by GPU budgets, benchmark–real-user distribution mismatch, English-centric data with unknown cross-lingual generalization, 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 recommender systems.