CIKM 2024 1st Workshop on Multimodal Search and Recommendations
Overview
This entry catalogs the CIKM 2024 1st Workshop on Multimodal Search and Recommendations (MMSR), the first edition of this workshop held in conjunction with the ACM Conference on Information and Knowledge Management (CIKM) 2024.
| Field | Details | |-------|---------| | Title | CIKM 2024 1st Workshop on Multimodal Search and Recommendations | | Link | https://cikm-mmsr.github.io/ | | Type | Conference / Workshop | | Category | Conferences, Workshops |
Background and Scope
The workshop addresses multimodal search and recommendation—jointly modeling text, image, video, and other modalities for information access and personalization. In the LLM era, traditional pipelines that separate retrieval, ranking, and generation struggle to meet user expectations for natural language interaction, multi-hop reasoning, and real-time knowledge. Key application scenarios include:
- Open-domain information access
- Enterprise knowledge retrieval
- Conversational search
- Semantic understanding in recommendation systems
- End-to-end architectures combining external knowledge sources with generative models
- A unified perspective that brings together scattered related work into a comparable framework
- A decomposition of method components: representation learning, retrievers, rerankers, planners, generators, and feedback mechanisms
- Reproducible benchmarks, datasets, or taxonomy tables
- Interfaces to emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration
- Explicit open problems: evaluation reliability, latency and cost, hallucination and safety, cross-lingual and multimodal scaling
- 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 and Personalization
- Official workshop page: https://cikm-mmsr.github.io/
Core Topics and Contributions
Workshops in this space typically contribute:
Typical Technical Pipeline
Methods in this area usually follow a four-step pattern:
1. Input and representation: encoding queries, documents, and user context into dense/sparse representations or 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-pass retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control
Key Insights for Search and Recommendation
1. Architecture: cascade retrieval + reranking + generation remains mainstream, but agentic paradigms are making "retrieval frequency and strategy" itself 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 should be cross-validated with human evaluation 4. Product: latency, cost, interpretability, and safety policies are hard constraints for industrial deployment—academic benchmarks alone are insufficient
Engineering Checklist
| Item | Question | Suggestion | |------|----------|------------| | Data | Does training/indexing contain PII? How are versions managed? | Partitioned indexes, anonymization, rollback-able embedding versions | | Latency | What is the p99 budget? How many retrieval steps? | Cascade + early stopping, caching hot queries, async reranking | | Quality | Do offline gains translate to online CTR/satisfaction? | Interleaving experiments, human audits, citation verification | | Safety | Does open retrieval introduce poisoning/bias? | Source whitelisting, adversarial detection, output filtering | | Cost | Token and GPU cost per query? | Small-model routing, distillation, hybrid sparse+dense retrieval |