Importance of Search Engines and Recommender Systems
Search engines and recommender systems play a crucial role in online content platforms today. Traditional search methods primarily focus on text, leaving a significant gap in effectively handling images and videos, which are vital in User-Generated Content (UGC) communities.
Challenges in Current Search and Recommendation Systems
Current datasets for search and recommendation tasks are limited to textual information or dense statistical features, hindering the development of effective multimodal services. Additionally, session-level signals contain valuable contextual information that can enhance user satisfaction and retention.
Existing Solutions
Some existing approaches have attempted to tackle multimodal retrieval challenges. Representation learning methods map images into binary spaces or encode them using deep neural networks. While hash-aware methods offer efficient real-time performance, semantic-based approaches focus on understanding different modalities and matching them effectively.
The Qilin Dataset
Researchers from Xiaohongshu Inc. and Tsinghua University have introduced Qilin, a multimodal information retrieval dataset aimed at improving search and recommendation services. Collected from Xiaohongshu, a popular social platform with over 300 million monthly active users, this dataset includes diverse user sessions with image-text notes, video notes, and commercial notes.
Key Features of Qilin
Qilin provides extensive APP-level contextual signals and genuine user feedback, which are essential for modeling user satisfaction and analyzing various user behaviors. The dataset construction involves user sampling, log joining, feature collection, and data filtering, resulting in a comprehensive resource for research.
Performance Insights
Initial results indicate that the BERT cross-encoder outperforms the bi-encoder in relevance matching, and Vision-Language Models (VLM) yield even better results by incorporating visual information. However, the performance gap in recommendation tasks highlights the need for greater model robustness.
Conclusion
The introduction of the Qilin dataset represents a significant advancement in multimodal information retrieval research. It addresses critical gaps in existing datasets and provides a rich framework for exploring various information retrieval tasks. Preliminary experiments demonstrate its versatility and potential applications.
Next Steps for Businesses
Explore how artificial intelligence can enhance your operations:
- Identify processes that can be automated.
- Determine key performance indicators (KPIs) to measure the impact of AI investments.
- Select customizable tools that align with your business objectives.
- Start with small projects, assess their effectiveness, and gradually expand AI usage.
Contact Us
If you need assistance with managing AI in your business, reach out to us at hello@itinai.ru. Connect with us on Telegram, X, and LinkedIn.
