Stop Playing the Guessing Game! Target-free User Simulation for Evaluating Conversational Recommender Systems

• URL: http://arxiv.org/abs/2411.16160v1
• Date: Mon, 25 Nov 2024 07:36:20 GMT
• Title: Stop Playing the Guessing Game! Target-free User Simulation for Evaluating Conversational Recommender Systems
• Authors: Sunghwan Kim, Tongyoung Kim, Kwangwook Seo, Jinyoung Yeo, Dongha Lee,
• Abstract summary: PEPPER is an evaluation protocol with target-free user simulators constructed from real-user interaction histories and reviews. PEPPER enables realistic user-CRS dialogues without falling into simplistic guessing games. PEPPER presents detailed measures for comprehensively evaluating the preference elicitation capabilities of CRSs.
• Score: 15.481944998961847
• License:
• Abstract: Recent approaches in Conversational Recommender Systems (CRSs) have tried to simulate real-world users engaging in conversations with CRSs to create more realistic testing environments that reflect the complexity of human-agent dialogue. Despite the significant advancements, reliably evaluating the capability of CRSs to elicit user preferences still faces a significant challenge. Existing evaluation metrics often rely on target-biased user simulators that assume users have predefined preferences, leading to interactions that devolve into simplistic guessing game. These simulators typically guide the CRS toward specific target items based on fixed attributes, limiting the dynamic exploration of user preferences and struggling to capture the evolving nature of real-user interactions. Additionally, current evaluation metrics are predominantly focused on single-turn recall of target items, neglecting the intermediate processes of preference elicitation. To address this, we introduce PEPPER, a novel CRS evaluation protocol with target-free user simulators constructed from real-user interaction histories and reviews. PEPPER enables realistic user-CRS dialogues without falling into simplistic guessing games, allowing users to gradually discover their preferences through enriched interactions, thereby providing a more accurate and reliable assessment of the CRS's ability to elicit personal preferences. Furthermore, PEPPER presents detailed measures for comprehensively evaluating the preference elicitation capabilities of CRSs, encompassing both quantitative and qualitative measures that capture four distinct aspects of the preference elicitation process. Through extensive experiments, we demonstrate the validity of PEPPER as a simulation environment and conduct a thorough analysis of how effectively existing CRSs perform in preference elicitation and recommendation.

Related papers

• Towards Empathetic Conversational Recommender Systems [77.53167131692]
  We propose an empathetic conversational recommender (ECR) framework. ECR contains two main modules: emotion-aware item recommendation and emotion-aligned response generation. Our experiments on the ReDial dataset validate the efficacy of our framework in enhancing recommendation accuracy and improving user satisfaction.
  arXiv  Detail & Related papers  (2024-08-30T15:43:07Z)
• Reformulating Conversational Recommender Systems as Tri-Phase Offline Policy Learning [5.453444582931813]
  Tri-Phase Offline Policy Learning-based Conversational Recommender System (TCRS) This paper introduces the Tri-Phase Offline Policy Learning-based Conversational Recommender System (TCRS)
  arXiv  Detail & Related papers  (2024-08-13T10:58:29Z)
• A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems [14.646529557978512]
  Conversational Recommender System (CRS) leverages real-time feedback from users to dynamically model their preferences. Large Language Models (LLMs) has marked the onset of a new epoch in computational capabilities. We introduce a Controllable, scalable, and human-Involved (CSHI) simulator framework that manages the behavior of user simulators.
  arXiv  Detail & Related papers  (2024-05-13T03:02:56Z)
• How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation [14.646529557978512]
  We analyze the limitations of using Large Language Models in constructing user simulators for Conversational Recommender System. Data leakage, which occurs in conversational history and the user simulator's replies, results in inflated evaluation results. We propose SimpleUserSim, employing a straightforward strategy to guide the topic toward the target items.
  arXiv  Detail & Related papers  (2024-03-25T04:21:06Z)
• A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems [54.78815548652424]
  Conversational recommender systems generate recommendations through an interactive process. Not all CRS approaches use human conversations as their source of interaction data. holistic CRS are trained using conversational data collected from real-world scenarios.
  arXiv  Detail & Related papers  (2023-09-14T12:55:23Z)
• Improving Conversational Recommendation Systems via Counterfactual Data Simulation [73.4526400381668]
  Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. We propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs.
  arXiv  Detail & Related papers  (2023-06-05T12:48:56Z)
• Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models [115.7508325840751]
  The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs) In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the existing evaluation protocol. We propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators.
  arXiv  Detail & Related papers  (2023-05-22T15:12:43Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)
• 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.