Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

• URL: http://arxiv.org/abs/2308.08496v1
• Date: Sat, 5 Aug 2023 22:50:21 GMT
• Title: Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review
• Authors: Siamak Farshidi, Kiyan Rezaee, Sara Mazaheri, Amir Hossein Rahimi, Ali Dadashzadeh, Morteza Ziabakhsh, Sadegh Eskandari, and Slinger Jansen
• Abstract summary: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. We developed a decision model to assist researchers in selecting the most suitable models for their systems. Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems.
• Score: 1.3630870408844922
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user's request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks.

Related papers

• Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithms [91.19304518033144]
  We aim to align vision models with human aesthetic standards in a retrieval system. We propose a preference-based reinforcement learning method that fine-tunes the vision models to better align the vision models with human aesthetics.
  arXiv  Detail & Related papers  (2024-06-13T17:59:20Z)
• A Survey of Latent Factor Models in Recommender Systems [0.0]
  This survey systematically reviews latent factor models in recommender systems. The literature is examined through a structured framework covering learning data, model architecture, learning strategies, and optimization techniques.
  arXiv  Detail & Related papers  (2024-05-28T11:28:59Z)
• Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform [64.76104135495576]
  We propose a novel anonymous crowd-sourcing evaluation platform, BingJian, for large language models. Through this platform, users have the opportunity to submit their questions, testing the models on a personalized and potentially broader range of capabilities.
  arXiv  Detail & Related papers  (2024-03-13T07:31:20Z)
• A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System [32.065158970382036]
  Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider. We developed a novel, interactive human-in-the-loop topic modeling system with a user-friendly interface.
  arXiv  Detail & Related papers  (2023-04-04T13:05:10Z)
• Deep Learning for Choice Modeling [5.173001988341294]
  We develop deep learning-based choice models under two settings of choice modeling: feature-free and feature-based. Our model captures both the intrinsic utility for each candidate choice and the effect that the assortment has on the choice probability.
  arXiv  Detail & Related papers  (2022-08-19T13:10:17Z)
• Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization [101.32332941117271]
  Decision making algorithms are used in a multitude of different applications. Deep learning approaches that use highly parametric architectures tuned from data without relying on mathematical models are becoming increasingly popular. Model-based optimization and data-centric deep learning are often considered to be distinct disciplines.
  arXiv  Detail & Related papers  (2022-05-05T13:40:08Z)
• Modeling User Behaviour in Research Paper Recommendation System [8.980876474818153]
  A user intention model is proposed based on deep sequential topic analysis. The model predicts a user's intention in terms of the topic of interest. The proposed approach introduces a new road map to model a user activity suitable for the design of a research paper recommendation system.
  arXiv  Detail & Related papers  (2021-07-16T11:31:03Z)
• 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)
• Enhancing Dialogue Generation via Multi-Level Contrastive Learning [57.005432249952406]
  We propose a multi-level contrastive learning paradigm to model the fine-grained quality of the responses with respect to the query. A Rank-aware (RC) network is designed to construct the multi-level contrastive optimization objectives. We build a Knowledge Inference (KI) component to capture the keyword knowledge from the reference during training and exploit such information to encourage the generation of informative words.
  arXiv  Detail & Related papers  (2020-09-19T02:41:04Z)
• Micro-entries: Encouraging Deeper Evaluation of Mental Models Over Time for Interactive Data Systems [7.578368459974474]
  We discuss the evaluation of users' mental models of system logic. Mental models are challenging to capture and analyze. By asking users to describe what they know and how they know it, researchers can collect structured, time-ordered insight.
  arXiv  Detail & Related papers  (2020-09-02T18:27:04Z)
• Explainable Recommender Systems via Resolving Learning Representations [57.24565012731325]
  Explanations could help improve user experience and discover system defects. We propose a novel explainable recommendation model through improving the transparency of the representation learning process.
  arXiv  Detail & Related papers  (2020-08-21T05:30:48Z)

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.