Conversational Recommenders

• "It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems
  Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g., “It doesn’t look goo…
• A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems
  Conversational recommender systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interaction data; the major…
• A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
  Conversational recommender systems (CRS) enable the traditional recommender systems to explicitly acquire user preferences towards items and attributes through interactive conversations. Reinforcement…
• Advances and Challenges in Conversational Recommender Systems: A Survey
  Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to a…
• Backtracing: Retrieving the Cause of the Query
  While information retrieval (IR) systems may provide answers for such user queries, they do not directly assist content creators—such as lecturers who want to improve their content—identify segments t…
• Conversational Recommendation: A Grand AI Challenge
  First, while conventional recommendations rely on push communication, conversational recommender systems (CRS) support multi-turn and mixed-initiative interaction patterns. Moreover, in particular nat…
• Dialoging Resonance: How Users Perceive, Reciprocate and React to Chatbot’s Self-Disclosure in Conversational Recommendations
  “Using chatbots to deliver recommendations is increasingly popular. The design of recommendation chatbots has primarily been taking an information-centric approach by focusing on the recommended conte…
• INSPIRED: Toward Sociable Recommendation Dialog Systems
  we present INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations. To better understand how humans make recommendations in communica…
• Improving Conversational Recommender Systems via Transformer-based Sequential Modelling
  In Conversational Recommender Systems (CRSs), conversations usually involve a set of related items and entities e.g., attributes of items. These items and entities are mentioned in order following the…
• Large Language Models as Zero-Shot Conversational Recommenders
  (1) Data: To gain insights into model behavior in “in-the-wild” conversational recommendation scenarios, we construct a new dataset of recommendation-related conversations by scraping a popular discus…
• Learning to Ask Appropriate Questions in Conversational Recommendation
  Conversational recommender systems (CRSs) have revolutionized the conventional recommendation paradigm by embracing dialogue agents to dynamically capture the fine-grained user preference. In a typica…
• Learning to Ask Critical Questions for Assisting Product Search
  In this paper, we propose a dual-learning model that hybrids the best from both implicit session feedback and proactively clarifying with users on the most critical questions. Hence, there are two br…
• Leveraging Large Language Models in Conversational Recommender Systems
  effectively leveraging LLMs within a CRS introduces new technical challenges, including properly understanding and controlling a complex conversation and retrieving from external sources of informatio…
• Multi-Task End-to-End Training Improves Conversational Recommendation
  “The modern recommendation systems found in commercial applications are largely based on implicit preferences, such as a user’s history of web page clicks, item purchases, or media streams, with the r…
• OpinionConv: Conversational Product Search with Grounded Opinions
  “When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also t…
• Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
  However, LLMs lack the knowledge of domain-specific item catalogs and behavioral patterns, particularly in areas that diverge from general world knowledge, such as online e-commerce. Finetuning LLMs f…
• RevCore: Review-augmented Conversational Recommendation
  CR remains challengeable because (i) typical dialogues are short and lack sufficient item information for user preference capturing (Chen et al., 2019; Zhou et al., 2020), and (ii) difficulties exist …
• Topic-Guided Conversational Recommender in Multiple Domains
  Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. To develop an effective CRS, the support of high-quality datasets is essential.…
• Towards Conversational Recommendation over Multi-Type Dialogs
  We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recomm…
• Towards Question-based Recommender Systems
  Conversational and question-based recommender systems have gained increasing attention in recent years, with users enabled to converse with the system and better control recommendations. Nevertheless,…
• Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
  Conversational recommender systems (CRS) enable the traditional recommender systems to explicitly acquire user preferences towards items and attributes through interactive conversations. Reinforcement…
• User-Centric Conversational Recommendation with Multi-Aspect User Modeling
  Conversational recommender systems (CRS) aim to provide high-quality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understanding of the current s…