Harmonizing Large Language Models with Collaborative Behavioral Signals for Conversational Recommendation

• URL: http://arxiv.org/abs/2503.10703v1
• Date: Wed, 12 Mar 2025 09:01:09 GMT
• Title: Harmonizing Large Language Models with Collaborative Behavioral Signals for Conversational Recommendation
• Authors: Guanrong Li, Kuo Tian, Jinnan Qi, Qinghan Fu, Zhen Wu, Xinyu Dai,
• Abstract summary: This work presents a novel probabilistic framework that synergizes behavioral patterns with conversational interactions through latent preference modeling.<n>The framework first derives latent preference representations through established collaborative filtering techniques, then employs these representations to jointly refine both the linguistic preference expressions and behavioral patterns.
• Score: 20.542601754190073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational recommendation frameworks have gained prominence as a dynamic paradigm for delivering personalized suggestions via interactive dialogues. The incorporation of advanced language understanding techniques has substantially improved the dialogue fluency of such systems. However, while modern language models demonstrate strong proficiency in interpreting user preferences articulated through natural conversation, they frequently encounter challenges in effectively utilizing collective behavioral patterns - a crucial element for generating relevant suggestions. To mitigate this limitation, this work presents a novel probabilistic framework that synergizes behavioral patterns with conversational interactions through latent preference modeling. The proposed method establishes a dual-channel alignment mechanism where implicit preference representations learned from collective user interactions serve as a connecting mechanism between behavioral data and linguistic expressions. Specifically, the framework first derives latent preference representations through established collaborative filtering techniques, then employs these representations to jointly refine both the linguistic preference expressions and behavioral patterns through an adaptive fusion process. Comprehensive evaluations across multiple benchmark datasets demonstrate the superior performance of the proposed approach compared to various state-of-the-art baseline methods, particularly in aligning conversational interactions with collaborative behavioral signals.

Related papers

• Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation [22.213312621287482]
  This paper proposes a novel model to introduce contextual disentanglement for improving conversational recommender systems. DisenCRS employs a dual disentanglement framework, including self-supervised contrastive disentanglement and counterfactual inference disentanglement. Experimental results on two widely used public datasets demonstrate that DisenCRS significantly outperforms existing conversational recommendation models.
  arXiv  Detail & Related papers  (2025-04-24T10:33:26Z)
• A Multi-view Discourse Framework for Integrating Semantic and Syntactic Features in Dialog Agents [0.0]
  Multiturn dialogue models aim to generate human-like responses by leveraging conversational context. Existing methods often neglect the interactions between these utterances or treat all of them as equally significant. This paper introduces a discourse-aware framework for response selection in retrieval-based dialogue systems.
  arXiv  Detail & Related papers  (2025-04-12T04:22:18Z)
• Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
  We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
  arXiv  Detail & Related papers  (2023-06-06T19:17:47Z)
• Pre-training Multi-party Dialogue Models with Latent Discourse Inference [85.9683181507206]
  We pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model.
  arXiv  Detail & Related papers  (2023-05-24T14:06:27Z)
• Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations [1.8126187844654875]
  We approach Emotion Recognition in Conversations leveraging the conversational context. We propose context-dependent embedding representations of each utterance. The effectiveness of our approach is validated on the open-domain DailyDialog dataset and on the task-oriented EmoWOZ dataset.
  arXiv  Detail & Related papers  (2023-04-17T12:37:57Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• Learning Locality and Isotropy in Dialogue Modeling [28.743212772593335]
  We propose a simple method for dialogue representation calibration, namely SimDRC, to build isotropic and conversational feature spaces. Experimental results show that our approach significantly outperforms the current state-of-the-art models on three dialogue tasks.
  arXiv  Detail & Related papers  (2022-05-29T06:48:53Z)
• DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling [3.3578533367912025]
  We propose a framework that incorporates augmented versions of a dialogue context into the learning objective. We show that our proposed augmentation method outperforms previous data augmentation approaches.
  arXiv  Detail & Related papers  (2022-04-15T23:39:41Z)
• Coreference-Aware Dialogue Summarization [24.986030179701405]
  We investigate approaches to explicitly incorporate coreference information in neural abstractive dialogue summarization models. Experimental results show that the proposed approaches achieve state-of-the-art performance. Evaluation results on factual correctness suggest such coreference-aware models are better at tracing the information flow among interlocutors.
  arXiv  Detail & Related papers  (2021-06-16T05:18:50Z)
• I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling [104.09033240889106]
  We introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues. We then compare a structured utterance-based approach of using pre-trained Transformer models for contradiction detection with the typical unstructured approach.
  arXiv  Detail & Related papers  (2020-12-24T18:47:49Z)
• Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues [88.73739515457116]
  We introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination. We jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. Experiment results indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection.
  arXiv  Detail & Related papers  (2020-09-14T08:44:46Z)
• Dialogue-Based Relation Extraction [53.2896545819799]
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
  arXiv  Detail & Related papers  (2020-04-17T03:51:57Z)

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.