Towards Conversational Recommender Systems

By Konstantina Christakopoulou, Filip Radlinski, Katja Hofmann. Knowledge Discovery and Data Mining, 2016

475 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/8e19e5e3005b133159a412d147943fcbee3ca246

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Citations in the corpus

A Survey on Conversational Recommender Systems (2020, methodology): “1,900 items with multiple features like cuisine, ratings, price, location, or parking [135]. Search logs and reviews of 3,549 users of a restaurant review provider, focusing on locations in Cambridge [29]. A travel destinations dataset, crawled from online platforms containing 5,723,169 venues in 18”
Towards Knowledge-Based Recommender Dialog System (2019, unknown): “Earlier studies in this ﬁeld focus on different tasks such as minimizing the number of user queries (Christakopoulou et al., 2016), training the dialog agent to ask for facet values for recommendation (Sun and Zhang, 2018).”
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion (2020, background): “Early conversational or interactive recommendation systems mainly utilized predefined actions to interact with users [5].”
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems (2020, methodology): “For example, [10, 38] only allow the CRS to recommend items without asking the user about their preferred attributes.”
A Theoretical Framework for Conversational Search (2017, background): “Yet such a slotdriven approach differs from human recommendation where it is rarely important to fill all slots [9].”
RecSim: A Configurable Simulation Platform for Recommender Systems (2019, methodology): “…use conversational methods [Vinyals and Le, 2015, Ghazvininejad et al., 2018], example critiquing or preference elicitation [Chen and Pu, 2012, Christakopoulou et al., 2016], bandit-based exploration [Li et al., 2010, 2016, Christakopoulou and Banerjee, 2018], or reinforcement learning [Sun…”
Personalized Reason Generation for Explainable Song Recommendation (2019, background): “We would like to make the user feel as if she is receiving a recommendation from her friend, since people often accept recommendations given by their friends [3].”
Towards Conversational Search and Recommendation: System Ask, User Respond (2018, background): “Christakopoulou et al [6] proposed an interactive recommendation protocol that collects like/dislike feedback from users to refine the recommendations.”
Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search (2020, background): “cation of clarification is in conversational recommendation systems, where the system asks about different attributes of the items to reveal the user preferences. For instance, Christakopoulou et al. [8] designed an interactive system for venue recommendation. Sun and Zhang [48] utilized facet-value”
Towards Deep Conversational Recommendations (2018, background): “using slot-value pairs [12, 13] or focus on different objectives such as minimizing the number of user queries to obtain good recommendations [14].”
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5) (2022, background): “Besides classical rating prediction and direct user-item matching-based recommendation for users, recent works are broadening the spectrum to new application scenarios such as sequential recommendation [20, 56, 59, 76], conversational recommendation [7, 57, 72], explainable recommendation [29, 58, 6”
Asking Clarifying Questions in Open-Domain Information-Seeking Conversations (2019, unknown): “For instance, Christakopoulou et al. [12] designed a system that can interact with users to collect more detailed information about their preferences in venue recommendation.”
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023, unknown): “When performings these instructions, LLMs act as a traditional recommender .”
Advances and Challenges in Conversational Recommender Systems: A Survey (2021, methodology): “Generally, interactive recommendations [231, 175, 204, 40], critiquing methods [23, 187, 107, 106], and CRSs focusing on the multi-turn conversation strategy [32, 31, 88, 89, 95] are prone to use the pre-annotated input and rule-based or templatebased output; dialogue systems [200, 220, 51] and CRSs”
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making (2018, background): “AI-supported human decision making is used, e.g., in recommender systems [1, 11, 37] or negotiating agents [24, 27].”
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System (2021, background): “Recent years have witnessed remarkable progress in the conversational recommender system (CRS) (Christakopoulou et al., 2016; Sun and Zhang, 2018; Li et al., 2018), which aims to provide high-quality recommendations to users through natural language conversations.”
Conversational Recommender System (2018, background): “us on improving rating prediction or ranking measures (learning to rank for recommendation) [7][18]. Few work has been done towards making recommendations in a dialogue system. Christakopoulou et al. [3] studied using a generative Gaussian model to recommend items to users in a conversation. However”