generative-retrieval

[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"

[Pytorch] Unofficial Implementation of "Recommender Systems with Generative Retrieval"

Updating curated list of research advancements on item identification and item tokenization in generative recommender systems. The survey is titled "A Survey of Item Identifiers in Generative Recommendation: Construction, Alignment, and Generation"

Official Implementation of GENIUS: A Generative Framework for Universal Multimodal Search, CVPR 2025

Differentiable Semantic ID for Generative Recommendation

We introduce the direct document relevance optimization (DDRO) for training a pairwise ranker model. DDRO encourages the model to focus on document-level relevance during generation

Code for "Multi-level Relevance Document Identifier Learning for Generative Retrieval". ACL 2025.

Code for "HierGR: Hierarchical Semantic Representation Enhancement for Generative Retrieval in Food Delivery Search". ACL 2025.

Reproducibility study of Planning-Ahead Generative Retrieval (PAG), probing lexical planner robustness under query perturbations and cross-lingual evaluation. SIGIR 2026 Reproducibility Track.

[Pytorch] Efficient tokenization for recommendations and generative retrieval. Inspired by STATIC decoding from "Vectorizing the Trie"

Generative-retrieval memory router (DSI: query→note-id) vs. embedding retrieval on a small, linked, franglais personal-memory corpus — with a hardened, confound-free eval. Reproducible.

The Integration of Chain-of-Thought in Generative Information Retrieval

Official code for ZeroGR: A Generalizable and Scalable Framework for Zero-Shot Generative Retrieval (arXiv:2510.10419)

Official code for "Generative Retrieval for Cover Song Identification via Audio-Derived Semantic IDs" (accepted at ICECCME 2026, IEEE)

SASRec с нуля на PyTorch (causal self-attention вручную) на Yandex Yambda. Аудио-эмбеддинги квантованы в semantic IDs через RQ-VAE: как замена item-эмбеддингов проигрывают, как дополнение дают +39% к NDCG@10 (0.575 vs 0.413). Ablation из шести моделей

Project for the University of Bologna "Cognitive Semantics for Artificial Intelligence" course (a.y. 2024-25).