PRISM introduces personalized item representations in user history, allowing each item’s embedding to reflect its relationship to a specific user rather than using a shared embedding across all users. The method leverages upstream ranking computations and an autoencoder to compress user and item tower states into a single embedding per scored pair for downstream replay. Offline tests on a billion‑scale recommendation surface show a modest but measurable improvement in downstream normalized entropy, with gains scaling with sequence length according to a power‑law relationship.
arXiv:2607. 12281v1 Announce Type: cross Abstract: Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely on per-token autoregressive prediction, which justifies maintaining large intermediate tensors that scale with sequence length.
arXiv:2511.22707v2 Announce Type: replace-cross Abstract: In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific item...
arXiv:2606. 25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors.
Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely on per-token autoregressive prediction, which justifies maintaining large intermediate tensors that scale with sequence length. In contrast, recommendation systems produce a single set of relevance scores for each pair without token-level supervision.
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.
BehaveRec is a three-signal framework that combines a causal session transformer, a bidirectional User History Encoder over a 90‑day window, and a GraphSAGE module built from behavioral embedding similarity. On an industrial dataset of 137M sequences, it improves R@5 by +3.2% over SASRec, and its advantage grows with user history length, becoming significant for users with 101+ interactions. The study also establishes a practical deployment rule of roughly 20 interactions per user for when long‑term history and behavioral‑graph signals are worthwhile.
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 DSRec, a dual‑interest sequential recommendation model that separates item representations into long‑term and short‑term semantic contexts. Long‑term embeddings capture stable preferences through historical aggregation, while short‑term embeddings focus on local session intent modulated by inter‑click time intervals. Each branch is processed by a distinct State Space Model— a full‑sequence Mamba for long‑term modeling and a time‑modulated SSM for short‑term dynamics— and a residual cross‑fusion mechanism aligns the two granularities while preserving their independence. Experiments on public benchmarks show that DSRec outperforms state‑of‑the‑art methods.
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.
arXiv:2606. 17276v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems.
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.