REVIEW 4 cited by
Multi-Task Deep Recommender Systems: A Survey
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
Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic in recommendation due to the demand for multi-task prediction considering performance and efficiency. Although MTL has been well studied and developed, there is still a lack of systematic review in the recommendation community. To fill the gap, we provide a comprehensive review of existing multi-task deep recommender systems (MTDRS) in this survey. To be specific, the problem definition of MTDRS is first given, and it is compared with other related areas. Next, the development of MTDRS is depicted and the taxonomy is introduced from the task relation and methodology aspects. Specifically, the task relation is categorized into parallel, cascaded, and auxiliary with main, while the methodology is grouped into parameter sharing, optimization, and training mechanism. The survey concludes by summarizing the application and public datasets of MTDRS and highlighting the challenges and future directions of the field.
Forward citations
Cited by 4 Pith papers
-
Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs
MME-SID improves LLM-based sequential recommendation by fusing collaborative, text, and image embeddings with quantized semantic IDs, using MMD reconstruction and code-embedding initialization.
-
Training-free LLM Merging for Multi-task Learning
Hi-Merging merges two task-specialized LLMs by pruning and scaling delta vectors at model and layer level, reporting gains over prior merging and multi-task fine-tuning on English and Chinese MCQA and QA tasks.
-
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
MESH integrates a GCN-based structural encoder with a frozen LLM-based semantic encoder via gated expert modules that adapt to historical and non-historical events, achieving modest gains on ICEWS14 and ICEWS18.
-
Macro Graph of Experts for Billion-Scale Multi-Task Recommendation
MGOE merges multi-task user-item graphs into a small macro graph and combines macro embeddings with mixture-of-experts towers, reporting offline and online gains that need stronger evaluation safeguards.
Discussion (0). Continue with ORCID to comment.