Papers with collaborative filtering
SVD-GCL: A Noise-Augmented Hybrid Graph Contrastive Learning Framework for Recommendation (2025.coling-main)
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| Challenge: | Recent advances in graph neural networks have made it difficult to capture user preferences. |
| Approach: | They propose a graph contrastive learning recommendation model based on noise augmentation that integrates truncated singular value decomposition in the feature engineering stage. |
| Outcome: | The proposed model reduces dimensionality and denoises the original data. |
Embedding Semantic Taxonomies (2020.coling-main)
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| Challenge: | Recent work on hierarchical representational structures in machine learning promises to blend the value of human curated taxonomies with the power and flexibility of machine learning systems. |
| Approach: | They propose to use box embeddings to encode aspects of partial ordering property of taxonomies to represent a medical subject headings taxonomy. |
| Outcome: | The proposed model outperforms baselines for taxonomic reconstruction and bipartite relationship experiments and is compared with a set of 300K PubMed articles with subject labels from MeSH. |
Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2026.acl-long)
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| Challenge: | Recent advances in large language models have enabled their use as semantic encoders for recommendation, but their roles and behaviors in this setting are still not well understood. |
| Approach: | They propose a benchmark to evaluate large language models as semantic encoders in recommendation scenarios. |
| Outcome: | The proposed benchmark shows that ranking of 11 leading LLMs is low compared to MTEB, highlighting the unique challenges of semantic encoding in recommendation. |
The steerability of large language models toward data-driven personas (2024.naacl-long)
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Junyi Li, Charith Peris, Ninareh Mehrabi, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard Zemel, Rahul Gupta
| Challenge: | Large language models generate biased responses where opinions of certain groups and populations are underrepresented. |
| Approach: | They propose a data-driven notion of persona that allows for a more nuanced understanding of different (latent) social groups present in the population. |
| Outcome: | The proposed method improves model steerability by 57% over baselines. |
RecLM: Recommendation Instruction Tuning (2025.acl-long)
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| Challenge: | Modern recommender systems aim to understand user-item relationships through past interactions, but their effectiveness is limited when handling sparse data or zero-shot scenarios. |
| Approach: | They propose a model-agnostic recommendation instruction-tuning paradigm that integrates large language models with collaborative filtering. |
| Outcome: | The proposed model-agnostic recommendation instruction-tuning paradigm improves performance across various settings and plug-and-play compatibility with state-of-the-art recommender systems. |
EasyRec: Simple yet Effective Language Models for Recommendation (2025.emnlp-main)
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| Challenge: | Existing methods for learning from user-item interaction data rely on unique user and item IDs, which limits their performance in zero-shot learning scenarios. |
| Approach: | They propose an approach that integrates text-based semantic understanding with collaborative signals. |
| Outcome: | The proposed approach outperforms state-of-the-art models in zero-shot recommendation scenarios. |
PepRec: Progressive Enhancement of Prompting for Recommendation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been gaining in-depth performance in natural language processing domains. |
| Approach: | They propose a training-free prompting framework that captures knowledge from content-based filtering and collaborative filtering to boost recommendation performance with LLMs. |
| Outcome: | The proposed framework outperforms traditional deep learning recommendation models and prompt-based recommendation systems on two real-world datasets. |
MemRec: Collaborative Memory-Augmented Agentic Recommender System (2026.acl-long)
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Weixin Chen, Yuhan Zhao, Jingyuan Huang, Zihe Ye, Mingxuan Ju, Tong Zhao, Neil Shah, Li Chen, Yongfeng Zhang
| Challenge: | Existing recommender systems rely on semantic user and item memories to make predictions, but these memories are kept in isolation. |
| Approach: | They propose a framework that architecturally decouples memory management from reasoning to decouple memory management and reasoning from the user and item memories. |
| Outcome: | The proposed framework decouples memory management from reasoning and achieves state-of-the-art performance on four benchmarks. |