UxSID is a framework for modeling ultra‑long user sequences that balances efficiency and effectiveness by using semantic‑group shared interest memory. It employs Semantic IDs (SIDs) and a dual‑level attention strategy to capture target‑aware preferences without the heavy cost of item‑specific models. The end‑to‑end architecture achieves state‑of‑the‑art performance and a 0.337% revenue lift in a large‑scale advertising A/B test.
arXiv:2606. 07546v1 Announce Type: cross Abstract: Capturing user interests across extensive watch histories is critical for short-form video recommendation, yet scaling sequence length is limited by two bottlenecks: the semantic sparsity of atomic Video IDs and the quadratic computational complexity of Transformers.
LIME is a new recommendation system architecture that tackles the high computational cost of transformers by introducing low‑rank link embeddings and a linear attention mechanism called LIME‑XOR. These innovations decouple user and candidate interactions, allowing pre‑computation of attention weights and reducing inference complexity to be nearly independent of candidate set size and linear in user sequence length. Experiments on public and industrial datasets demonstrate that LIME matches transformer performance while achieving a tenfold speedup on large candidate sets or long sequences, and it has been shown to improve user engagement on a major recommendation platform.
EPIC: Explicit Posterior Item Conditioning for Semantic ID Diffusion Recommendation proposes a method that adds explicit item-level competition into the denoising process of Semantic ID (SID) generative recommendation. The approach builds a personalized posterior over feasible candidate items based on the current generation context and the user's recent interactions, then projects this distribution back to unresolved SID positions to guide subsequent token decisions. Experiments on four Amazon benchmarks show consistent improvements over strong baselines, with diagnostic analyses indicating that the gains stem from personalized transition evidence that preserves promising item hypotheses during denoising.
arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.
arXiv:2607. 25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization.
The paper introduces MARS, a multi‑resolution adaptive routing system for sequential recommendation that stores a user’s entire history in recurrent state tracks with different half‑lives. By sparsely routing queries to the appropriate temporal resolution, MARS preserves short‑term intent, medium‑term interests, and long‑term preferences, mitigating the temporal aliasing problem seen in monolithic cached memories. Experiments on three public datasets show that MARS outperforms strong baselines, with larger gains for longer histories and after user behavioral shifts, while maintaining comparable serving latency.
arXiv:2607. 28659v1 Announce Type: new Abstract: Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains.
Merge and Branch (M&B) is a tree‑structured supervised fine‑tuning method for large language models (LLMs) in recommendation systems. It compresses each historical item’s semantic identifier (SID) and text into a few learned tokens (Merge) and then uses the compressed history to predict multiple target SIDs (Branch), organizing each example as a shallow, wide tree. The approach includes a tree FlashAttention mechanism for efficient training and a tree‑structured key/value cache for constrained decoding, achieving better recommendation performance and improved training and decoding efficiency compared to strong baselines.
arXiv:2509. 25522v3 Announce Type: replace Abstract: Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to unify rich item semantics and collaborative filtering signals.
arXiv:2512. 10388v3 Announce Type: replace-cross Abstract: Conventional Sequential Recommender Systems (SRS) typically assign unique hash IDs (HID) to construct item embeddings, which mainly capture collaborative signals from historical user-item interactions.
The paper introduces GR4AD‑X, an enhanced generative recommendation framework for large‑scale advertising that tackles three key limitations of its predecessor: unreliable deterministic SID assignment, limited scalability of backbone models, and weak credit assignment in sequence‑level value optimization. GR4AD‑X improves SID reliability via Multi‑Path assignment, expands model capacity across complementary dimensions for better efficiency, and aligns generation with commercial value through Unified Value‑Guided Generation and Discrimination (UniVGD), which optimizes prefix‑level rewards, prunes beams, and reranks discriminatively. Online A/B tests on Kuaishou’s advertising platform show a 3.07% revenue increase over the baseline while meeting strict latency and throughput constraints.