A Comprehensive Guide to Generative Recommenders, Efficient Compression for Multi-Vector Retrieval , and More!
Vol.71 for Sep 23 - Sep 29, 2024
Stay Ahead of the Curve with the Latest Advancements and Discoveries in Information Retrieval.
This week’s newsletter highlights the following research:
A Comprehensive Guide to Generative Recommender Systems, from Deldjoo et al.
A Survey of Data Augmentation Techniques in Sequential Recommendation Systems, from Dang et al.
A Systematic Approach to Building Domain-Specific LLM Applications, from Microsoft
Efficient Compression for Multi-Vector Retrieval Systems, from Clavié et al.
A Critical Re-examination of Bayesian Personalized Ranking, from Milogradskii et al.
Leveraging LLM's In-Context Learning for State-of-the-Art Text Embeddings, from Li et al.
Enhancing Conversational QA with Adaptive Retrieval and Context Summarization, from Roy et al.
Efficient Multimodal Recommendation via Matryoshka Representation Learning, from Wang et al.
A Comprehensive Survey of Contextual Compression in Retrieval-Augmented Generation, from IBM
An Approach to Integrating Time-Varied Behavioral Features in Product Search, from Walmart
