Co-NAML-LSTUR: A Combined Model with Attentive Multi-View Learning and Long- and Short-term User Representations for News Recommendation

• URL: http://arxiv.org/abs/2507.20210v1
• Date: Sun, 27 Jul 2025 10:18:22 GMT
• Title: Co-NAML-LSTUR: A Combined Model with Attentive Multi-View Learning and Long- and Short-term User Representations for News Recommendation
• Authors: Minh Hoang Nguyen, Thuat Thien Nguyen, Minh Nhat Ta,
• Abstract summary: News recommendation systems play a vital role in mitigating information overload by delivering personalized news content.<n>A central challenge is to effectively model both multi-view news representations and the dynamic nature of user interests.<n>We propose Co-NAML-LSTUR, a hybrid news recommendation framework that integrates NAML for attentive multi-view news modeling and LSTUR for capturing user preferences.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: News recommendation systems play a vital role in mitigating information overload by delivering personalized news content. A central challenge is to effectively model both multi-view news representations and the dynamic nature of user interests, which often span both short- and long-term preferences. Existing methods typically rely on single-view features of news articles (e.g., titles or categories) or fail to comprehensively capture user preferences across time scales. In this work, we propose Co-NAML-LSTUR, a hybrid news recommendation framework that integrates NAML for attentive multi-view news modeling and LSTUR for capturing both long- and short-term user representations. Our model also incorporates BERT-based word embeddings to enhance semantic feature extraction. We evaluate Co-NAML-LSTUR on two widely used benchmarks, MIND-small and MIND-large. Experimental results show that Co-NAML-LSTUR achieves substantial improvements over most state-of-the-art baselines on MIND-small and MIND-large, respectively. These results demonstrate the effectiveness of combining multi-view news representations with dual-scale user modeling. The implementation of our model is publicly available at https://github.com/MinhNguyenDS/Co-NAML-LSTUR.

Related papers

• Unifying Multimodal Large Language Model Capabilities and Modalities via Model Merging [103.98582374569789]
  Model merging aims to combine multiple expert models into a single model, thereby reducing storage and serving costs.<n>Previous studies have primarily focused on merging visual classification models or Large Language Models (LLMs) for code and math tasks.<n>We introduce the model merging benchmark for MLLMs, which includes multiple tasks such as VQA, Geometry, Chart, OCR, and Grounding, providing both LoRA and full fine-tuning models.
  arXiv  Detail & Related papers  (2025-05-26T12:23:14Z)
• Enhancing News Recommendation with Hierarchical LLM Prompting [17.481812986550633]
  We introduce PNR-LLM, for Large Language Models for Personalized News Recommendation.<n>PNR-LLM harnesses the generation capabilities of LLMs to enrich news titles and abstracts.<n>We propose an attention mechanism to aggregate enriched semantic- and entity-level data, forming unified user and news embeddings.
  arXiv  Detail & Related papers  (2025-04-29T06:02:16Z)
• Exploring Multi-Grained Concept Annotations for Multimodal Large Language Models [55.25892137362187]
  We introduce a new dataset featuring Multimodal Multi-Grained Concept annotations (MMGiC) for MLLMs.<n>Our analyses reveal that multi-grained concept annotations integrate and complement each other, under our structured template and a general MLLM framework.<n>We further validate our hypothesis by investigating the fair comparison and effective collaboration between MMGiC and image--caption data on 12 multimodal comprehension and generation benchmarks.
  arXiv  Detail & Related papers  (2024-12-08T13:45:44Z)
• LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation [54.396000434574454]
  We propose a novel multi-interest SR framework combining implicit behavioral and explicit semantic perspectives.<n>It includes two modules: the Implicit Behavioral Interest Module and the Explicit Semantic Interest Module.<n>Experiments on four real-world datasets validate the framework's effectiveness and practicality.
  arXiv  Detail & Related papers  (2024-11-14T13:00:23Z)
• Personalized News Recommendation System via LLM Embedding and Co-Occurrence Patterns [6.4561443264763625]
  In news recommendation (NR), systems must comprehend and process a vast amount of clicked news text to infer the probability of candidate news clicks. In this paper, we propose a novel NR algorithm to reshape the news model via LLM Embedding and Co-Occurrence Pattern (LECOP) Extensive experiments demonstrate the superior performance of our proposed novel method.
  arXiv  Detail & Related papers  (2024-11-09T03:01:49Z)
• Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations [13.878297630442674]
  This paper introduces a novel framework for multi-behavior recommendations, leveraging the fusion of triple-modality.<n>Our proposed model called Triple Modality Fusion (TMF) utilizes the power of large language models (LLMs) to align and integrate these three modalities.<n>Extensive experiments demonstrate the effectiveness of our approach in improving recommendation accuracy.
  arXiv  Detail & Related papers  (2024-10-16T04:44:15Z)
• NoteLLM-2: Multimodal Large Representation Models for Recommendation [71.87790090964734]
  Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks.<n>Their potential in multimodal representation, particularly for item-to-item (I2I) recommendations, remains underexplored.<n>We propose an end-to-end fine-tuning method that customizes the integration of any existing LLMs and vision encoders for efficient multimodal representation.
  arXiv  Detail & Related papers  (2024-05-27T03:24:01Z)
• VLSNR:Vision-Linguistics Coordination Time Sequence-aware News Recommendation [0.0]
  multimodal semantics is beneficial for enhancing the comprehension of users' temporal and long-lasting interests. In our work, we propose a vision-linguistics coordinate time sequence news recommendation. We also construct a large scale multimodal news recommendation dataset V-MIND.
  arXiv  Detail & Related papers  (2022-10-06T14:27:37Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles [63.32111010686954]
  We propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) The main challenge in this task is to jointly model the temporal dependency of video with semantic meaning of article. We propose a Dual-Interaction-based Multimodal Summarizer (DIMS), consisting of a dual interaction module and multimodal generator.
  arXiv  Detail & Related papers  (2020-10-12T02:19:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.