Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge

• URL: http://arxiv.org/abs/2209.11386v1
• Date: Fri, 23 Sep 2022 03:30:22 GMT
• Title: Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge
• Authors: Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong
• Abstract summary: We propose to fully discover and extract internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user preferences for the recommendation. Our model achieves better performance on most evaluation metrics with less external knowledge and generalizes well to other domains.
• Score: 25.503407835218773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of the external domain-specific information needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user preferences for the recommendation, where a time-aware attention is designed to emphasize the recently appeared items in entity-level representations. We further use the pre-trained BART to initialize the generation module to alleviate the data scarcity and enhance the context modeling. In addition to conducting experiments on a popular dataset (ReDial), we also include a multi-domain dataset (OpenDialKG) to show the effectiveness of our model. Experiments on both datasets show that our model achieves better performance on most evaluation metrics with less external knowledge and generalizes well to other domains. Additional analyses on the recommendation and generation tasks demonstrate the effectiveness of our model in different scenarios.

Related papers

• Making Recommender Systems More Knowledgeable: A Framework to Incorporate Side Information [5.033504076393256]
  We propose a general framework for incorporating item-specific side information into the recommender system to enhance its performance. We show that with side information, our recommender system outperforms state-of-the-art models by a considerable margin. We also propose a new type of loss to regularize the attention mechanism used by recommender systems and evaluate its influence on model performance.
  arXiv  Detail & Related papers  (2024-06-02T04:33:52Z)
• Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph [19.201697767418597]
  We propose a novel Large Language Model based Complementary Knowledge Enhanced Recommendation System (LLM-KERec) It extracts unified concept terms from item and user information to capture user intent transitions and adapt to new items. Extensive experiments conducted on three industry datasets demonstrate the significant performance improvement of our model compared to existing approaches.
  arXiv  Detail & Related papers  (2024-02-21T12:22:01Z)
• Learning to Extract Structured Entities Using Language Models [52.281701191329]
  Recent advances in machine learning have significantly impacted the field of information extraction. We reformulate the task to be entity-centric, enabling the use of diverse metrics. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP metric.
  arXiv  Detail & Related papers  (2024-02-06T22:15:09Z)
• Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
  We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems. KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities. KERL achieves state-of-the-art results in both recommendation and response generation tasks.
  arXiv  Detail & Related papers  (2023-12-18T06:41:23Z)
• Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation [28.349599213528627]
  Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. In this study, we delve into benchmark datasets for developing CRS models and address potential biases arising from the feedback loop inherent in multi-turn interactions. We present two novel strategies, 'Once-Aug' and 'PopNudge', to enhance model performance while mitigating biases.
  arXiv  Detail & Related papers  (2023-10-25T16:11:55Z)
• Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling [18.297332953450514]
  We propose LANCER, which leverages the semantic understanding capabilities of pre-trained language models to generate personalized recommendations. Our approach bridges the gap between language models and recommender systems, resulting in more human-like recommendations.
  arXiv  Detail & Related papers  (2023-09-19T08:54:47Z)
• Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling [96.75821232222201]
  Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. We propose a novel framework that simultaneously implements the idea of internal-information screening and external-information exploiting.
  arXiv  Detail & Related papers  (2023-05-19T14:56:57Z)
• MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective [57.19660234992812]
  NER model has achieved promising performance on standard NER benchmarks. Recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition. We propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective.
  arXiv  Detail & Related papers  (2022-04-09T05:18:20Z)
• C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System [47.18484863699936]
  We propose a novel contrastive learning framework to improve data semantic fusion for Conversational recommender systems. In our approach, we first extract and represent multi-grained semantic units from different data signals, and then align the associated multi-type semantic units in a coarse-to-fine way. Experiments on two public CRS datasets have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.
  arXiv  Detail & Related papers  (2022-01-04T11:39:41Z)
• A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation [70.69134448863483]
  Research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models.
  arXiv  Detail & Related papers  (2021-04-27T08:03:52Z)
• Leveraging Historical Interaction Data for Improving Conversational Recommender System [105.90963882850265]
  We propose a novel pre-training approach to integrate item- and attribute-based preference sequence. Experiment results on two real-world datasets have demonstrated the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-08-19T03:43:50Z)

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.