The DOI matches its Crossref record; 25 of the 27 references it cites were found in a registry or an index.
Conversational Recommender System
Open accessAbstract
A personalized conversational sales agent could have much commercial potential. E-commerce companies such as Amazon, eBay, JD, Alibaba etc. are piloting such kind of agents with their users. However, the research on this topic is very limited and existing solutions are either based on single round adhoc search engine or traditional multi round dialog system. They usually only utilize user inputs in the current session, ignoring users' long term preferences. On the other hand, it is well known that sales conversion rate can be greatly improved based on recommender systems, which learn user preferences based on past purchasing behavior and optimize business oriented metrics such as conversion rate or expected revenue. In this work, we propose to integrate research in dialog systems and recommender systems into a novel and unified deep reinforcement learning framework to build a personalized conversational recommendation agent that optimizes a per session based utility function. In particular, we propose to represent a user conversation history as a semi-structured user query with facet-value pairs. This query is generated and updated by belief tracker that analyzes natural language utterances of user at each step. We propose a set of machine actions tailored for recommendation agents and train a deep policy network to decide which action (i.e. asking for the value of a facet or making a recommendation) the agent should take at each step. We train a personalized recommendation model that uses both the user's past ratings and user query collected in the current conversational session when making rating predictions and generating recommendations. Such a conversational system often tries to collect user preferences by asking questions. Once enough user preference is collected, it makes personalized recommendations to the user. We perform both simulation experiments and real online user studies to demonstrate the effectiveness of the proposed framework.
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In the citing paper
“We found that existing NLP datasets do not cover the wide breadth of possible conversational failure cases due to their emphasis on correct data for training.”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [57]
In the citing paper
“Conversational Recommender Systems [24, 25] mark a remarkable stride forward in the realm of personalized assistance. By engaging users in interactive conversations, these systems delve deeper into their preferences and fine-tune their recommendations accordingly.”
From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [24]
In the citing paper
“Based on the classification of dialogue systems, common approaches in conversational recommender systems can also be divided into two categories: attribute-based QA (questionanswering) and generative methods.”
From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [24]
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Metadata
- Authors
- Sun, Y., & Zhang, Y.
- Affiliations
- University of California, Santa Cruz (US)
- ORCID
- Zhang, Yi
- Venue
- The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
- Publisher
- ACM
- Published
- 2018-06-27
- Pages
- 235-244
- OpenAlex
- W2964112275
- Cited by
- 378
- References
- 27
- Citations by year
- 2026: 16 · 2025: 40 · 2024: 50 · 2023: 67 · 2022: 61 · 2021: 69 · 2020: 48 · 2019: 25 · 2018: 2
- FWCI
- 28.25
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- conversational recommender systems, personalized recommendation, deep reinforcement learning, dialogue systems, salespeople, e-commerce, long-term user preferences, belief tracking, rating prediction, conversion optimization
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