Papers with recommendation task
A Multi-modal Large Language Model with Graph-of-Thought for Effective Recommendation (2025.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated a remarkable capability in language understanding and text generation in various realworld scenarios. |
| Approach: | They propose a Graph-of-Thought prompting technique in a Multi-modal LLM to leverage the complex structure of interaction graphs. |
| Outcome: | The proposed model outperforms 12 existing state-of-the-art models on 6 benchmark datasets. |
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods to study the Matthew effect in Recommender Systems (RSs) however, it is amplified when the user interacts with the system over time. |
| Approach: | They propose a paradigm to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences. |
| Outcome: | The proposed paradigm achieves state-of-the-art performance and superior of alleviating Matthew effect in conversational recommendation tasks. |
A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis (2022.lrec-1)
Copied to clipboard
| Challenge: | Graph databases are well-suited for crossreferencing information from multiple sources to support machine learning tasks. |
| Approach: | They propose a graph-based structure of multiple resources enriched with graph analytics approaches to provide an encompassing view of the movie recommendation domain and of the way people talk about it during the recommendation task. |
| Outcome: | The proposed graph-based structure provides an encompassing view of the domain and of the way people talk about it during the recommendation task. |
Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies show that knowledge graphs are incomplete since they do not contain all factual information present on the web. |
| Approach: | They propose to use knowledge graphs to improve the performance of conversational recommender systems by incorporating pre-trained embeddings from subgraphs and positional embeddments into their models. |
| Outcome: | The proposed method improves by 5.62% over the state-of-the-art method on multiple metrics on the recommendation task. |
A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature (D19-1)
Copied to clipboard
| Challenge: | Existing academic search engines cannot detect relevant papers where a resource is mentioned. |
| Approach: | They propose a framework to model the role and function of on-line resource citations . they construct a dataset SciRes, which includes 3,088 manually annotated resource contexts based on a multi-task framework . |
| Outcome: | The proposed model achieves the best results on both the classification task and recommendation task. |
RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation (2025.emnlp-main)
Copied to clipboard
Sashuai Zhou, Weinan Gan, Qijiong Liu, Ke Lei, Jieming Zhu, Hai Huang, Yan Xia, Ruiming Tang, Zhenhua Dong, Zhou Zhao
| Challenge: | Existing methods for addressing item-level user interests are lacking in cross-domain generalization . RecBase model is domain-agnostic and can be used to enhance recommender systems' effectiveness . |
| Approach: | They propose a domain-agnostic foundational model pretrained with a recommendation-oriented objective that leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross- domain generalization. |
| Outcome: | The proposed model matches or surpasses baselines in zero-shot and cross-domain recommendation tasks on eight real-world datasets. |
A Text-Based Recommender System that Leverages Explicit Affective State Preferences (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems that leverage user preferences that are implicit in user-item rating histories can be slow to track changes in user preferences and imprecise for users with diverse preferences. |
| Approach: | They propose a novel recommendation task that leverages a wide range of affective states sought explicitly by the user to identify items that induce those affective state. |
| Outcome: | The proposed model can leverage a wide range of affective states sought explicitly by the user to identify items likely to induce those affective state. |