Conversational recommender systems (CRS) are interactive agents that support their users in recommendation-related goals through multi-turn conversations. Generally, a CRS can be evaluated in various dimensions. Today's CRS mainly rely on offline(computational) measures to assess the performance of their algorithms in comparison to different baselines. However, offline measures can have limitations, for example, when the metrics for comparing a newly generated response with a ground truth do not correlate with human perceptions, because various alternative generated responses might be suitable too in a given dialog situation. Current research on machine learning-based CRS models therefore acknowledges the importance of humans in the evaluation process, knowing that pure offline measures may not be sufficient in evaluating a highly interactive system like a CRS.
Harbin Institute of Technology（哈尔滨工业大学）；Baidu Inc.（百度公司）
In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation. The difference between DuRecDial 2.0 and existing conversational recommendation datasets is that the data item (Profile, Goal, Knowledge, Context, Response) in DuRecDial 2.0 is annotated in two languages, both English and Chinese, while other datasets are built with the setting of a single language. We collect 8.2k dialogs aligned across English and Chinese languages (16.5…
Harbin Institute of Technology（哈尔滨工业大学）；Baidu Inc.（百度公司）
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 recommendation dialog, taking into account user's interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset \emph{DuRecDial} (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to…
Conversational recommender systems (CRS) that are able to interact with users in natural language often utilize recommendation dialogs which were previously collected with the help of paired humans, where one plays the role of a seeker and the other as a recommender. These recommendation dialogs include items and entities that indicate the users' preferences. In order to precisely model the seekers' preferences and respond consistently, CRS typically rely on item and entity annotations. A recent example of such a dataset is INSPIRED, which consists of recommendation dialogs for sociable conver…
Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation
Georgia Institute of Technology（佐治亚理工学院）；Columbia University（哥伦比亚大学）；California State University（加州州立大学）；University of Montreal（蒙特利尔大学）；The University of Manchester（曼彻斯特大学）；McGill University（麦吉尔大学）
Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Amsterdam（阿姆斯特丹大学）
Most conversational recommendation approaches are either not explainable, or they require external user's knowledge for explaining or their explanations cannot be applied in real time due to computational limitations. In this work, we present a real time category based conversational recommendation approach, which can provide concise explanations without prior user knowledge being required. We first perform an explainable user model in the form of preferences over the items' categories, and then use the category preferences to recommend items. The user model is performed by applying a BERT-bas…
University of Toronto（多伦多大学）；Deakin University（迪肯大学）；Vector Institute of Artificial Intelligence（向量人工智能研究院）
Conversational Recommendation Systems (CRSs) have recently started to leverage pretrained language models (LM) such as BERT for their ability to semantically interpret a wide range of preference statement variations. However, pretrained LMs are well-known to be prone to intrinsic biases in their training data, which may be exacerbated by biases embedded in domain-specific language data(e.g., user reviews) used to fine-tune LMs for CRSs. We study a recently introduced LM-driven recommendation backbone (termed LMRec) of a CRS to investigate how unintended bias i.e., language variations such as n…
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
Sun Yat-sen University（中山大学）；Peng Cheng Laboratory（鹏城实验室）；South China Agricultural University（华南农业大学）；Guangdong University of Technology（广东工业大学）；Jinan University（暨南大学）
Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring
We have developed a conversational recommendation system designed to help users navigate through a set of limited options to find the best choice. Unlike many internet scale systems that use a singular set of search terms and return a ranked list of options from amongst thousands, our system uses multi-turn user dialog to deeply understand the users preferences. The system responds in context to the users specific and immediate feedback to make sequential recommendations. We envision our system would be highly useful in situations with intrinsic constraints, such as finding the right restauran…
University of Klagenfurt（克拉根福大学）；Hong Kong Baptist University（香港浸会大学）
Animated avatars, which look and talk like humans, are iconic visions of the future of AI-powered systems. Through many sci-fi movies we are acquainted with the idea of speaking to such virtual personalities as if they were humans. Today, we talk more and more to machines like Apple's Siri, e.g., to ask them for the weather forecast. However, when asked for recommendations, e.g., for a restaurant to go to, the limitations of such devices quickly become obvious. They do not engage in a conversation to find out what we might prefer, they often do not provide explanations for what they recommend,…
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京市大数据管理与分析方法重点实验室）；School of Information, Renmin University of China（中国人民大学信息学院）；Renmin University of China（中国人民大学）
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. Existing CRS datasets mainly focus on immediate requests from users, while lack proactive guidance to the recommendation scenario. In this paper, we contribute a new CRS dataset named \textbf{TG-ReDial} (\textbf{Re}commendation through \textbf{T}opic-\textbf{G}uided \textbf{Dial}og). Our dataset has two major features. First, it incorporates topic threads to enforce natural semantic transitions t…
Research has shown that human-agent relationships form in similar ways to human-human relationships. Since children do not have the same critical analysis skills as adults (and may over-trust technology, for example), this relationship-formation is concerning. Nonetheless, little research investigates children's perceptions of conversational agents in-depth, and even less investigates how education might change these perceptions. We present K-12 workshops with associated conversational AI concepts to encourage healthier understanding and relationships with agents. Through studies with the curr…
Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Singapore Management University（新加坡管理大学）；Hong Kong Baptist University（香港浸会大学）；Huazhong University of Science and Technology（华中科技大学）
Natural language interfaces (NLIs) have become a prevalent medium for conducting visual data analysis, enabling people with varying levels of analytic experience to ask questions of and interact with their data. While there have been notable improvements with respect to language understanding capabilities in these systems, fundamental user experience and interaction challenges including the lack of analytic guidance (i.e., knowing what aspects of the data to consider) and discoverability of natural language input (i.e., knowing how to phrase input utterances) persist. To address these challeng…
