REVIEW 4 cited by
A Survey on User Behavior Modeling in Recommender Systems
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and items have been exploited, which brings compelling improvements in many recommendation tasks. In this paper, we attempt to provide a thorough survey of this research topic. We start by reviewing the research background of UBM. Then, we provide a systematic taxonomy of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. Within each direction, representative models and their strengths and weaknesses are comprehensively discussed. Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions. Finally, we summarize the survey and discuss the future prospects of this field.
Forward citations
Cited by 4 Pith papers
-
ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors
ShiJianBench uses matched counterfactual rollouts of a calibrated LLM investor simulator to show that response quality and long-horizon investment impact are distinct.
-
CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMs
CURP represents users as sparse combinations of discrete prototype codebook embeddings and uses them as frozen-LLM prefixes, outperforming personalization baselines on four text-generation tasks with about 20M trainab...
-
MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation
MISS builds a k-means index tree on interaction-supervised multi-modal embeddings and adds two behavior search units (Co-GSU, MM-GSU) plus ESU/MMoE, reporting ~30-47% relative recall gains over TDM+MMoE on Kuaishou da...
-
Coarse-to-Fine Long-term Interest Modeling for Generative Recommendation
GLASS extends generative retrieval with a tiered long-term interest vector and a first-SID-keyed search of long histories, reporting consistent gains over Tiger and DualGR on two public datasets.
Discussion (0). Sign in to comment.