12 papers
Forging More Tasks into Better Harnesses: Agentic Harness Evolution via Successive Scale Racing
Jingzhe Liu, Jiliang Tang, Tong Zhao +2
Agentic harness evolution optimizes an agent's instructions, tools, and control flow by using its task execution experiences as evolving evidence. While more evolution tasks should…
LLM-Based Generative Retrieval for Snapchat Content Recommendation
Liam Collins, Jiwen Ren, Donald Loveland +20
Pretrained large language models (LLMs) are promising retrieval engines because they combine rich semantic priors, strong sequence modeling capabilities, and favorable scaling beha…
On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies
Sunwoo Kim, Sunkyung Lee, Clark Mingxuan Ju +5
Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been increasingly adopted for GR, as their…
Self-supervised User Profile Generation for Personalization
Clark Mingxuan Ju, Yuwei Qiu, Tong Zhao +1
Personalizing large language models (LLMs) has become a central challenge as LLMs are deployed across recommendation, search, dialogue, and content generation -- settings where the…
MLPs are Efficient Distilled Generative Recommenders
Zitian Guo, Yupeng Hou, Clark Mingxuan Ju +2
Generative recommendation models employing Semantic IDs (SIDs) exhibit strong potential, yet their practical deployment is bottlenecked by the high inference latency of beam-expand…
Expressiveness Limits of Autoregressive Semantic ID Generation in Generative Recommendation
Yupeng Hou, Haven Kim, Clark Mingxuan Ju +3
Generative recommendation (GR) models generate items by autoregressively producing a sequence of discrete tokens that jointly index the target item. However, this autoregressive ge…
Semantic IDs for Recommender Systems at Snapchat: Use Cases, Technical Challenges, and Design Choices
Clark Mingxuan Ju, Tong Zhao, Leonardo Neves +15
Effective item identifiers (IDs) are an important component for recommender systems (RecSys) in practice, and are commonly adopted in many use cases such as retrieval and ranking.…
Exploiting ID-Text Complementarity via Ensembling for Sequential Recommendation
Liam Collins, Bhuvesh Kumar, Clark Mingxuan Ju +4
Modern Sequential Recommendation (SR) models commonly utilize modality features to represent items, motivated in large part by recent advancements in language and vision modeling.…
MemRec: Collaborative Memory-Augmented Agentic Recommender System
Weixin Chen, Yuhan Zhao, Jingyuan Huang +6
The evolution of recommender systems has shifted from traditional collaborative filtering to LLM-based agentic systems, which rely on semantic user and item memories to make predic…
Generative Recommendation with Semantic IDs: A Practitioner's Handbook
Clark Mingxuan Ju, Liam Collins, Leonardo Neves +4
Generative recommendation (GR) has gained increasing attention for its promising performance compared to traditional models. A key factor contributing to the success of GR is the s…
Revisiting Self-attention for Cross-domain Sequential Recommendation
Clark Mingxuan Ju, Leonardo Neves, Bhuvesh Kumar +7
Sequential recommendation is a popular paradigm in modern recommender systems. In particular, one challenging problem in this space is cross-domain sequential recommendation (CDSR)…
Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat
Clark Mingxuan Ju, Leonardo Neves, Bhuvesh Kumar +11
The development of powerful user representations is a key factor in the success of recommender systems (RecSys). Online platforms employ a range of RecSys techniques to personalize…