Snippet-based Conversational Recommender System

• URL: http://arxiv.org/abs/2411.06064v1
• Date: Sat, 09 Nov 2024 04:23:58 GMT
• Title: Snippet-based Conversational Recommender System
• Authors: Haibo Sun, Naoki Otani, Hannah Kim, Dan Zhang, Nikita Bhutani,
• Abstract summary: We introduce SnipRec, a novel CRS that enhances dialogues and recommendations by extracting diverse expressions and preferences from user-generated content (UGC) like customer reviews. Our approach eliminates the need for domain-specific training, making it adaptable to new domains and effective without prior knowledge of user preferences.
• Score: 7.943863017830094
• License:
• Abstract: Conversational Recommender Systems (CRS) engage users in interactive dialogues to gather preferences and provide personalized recommendations. Traditionally, CRS rely on pre-defined attributes or expensive, domain-specific annotated datasets to guide conversations, which limits flexibility and adaptability across domains. In this work, we introduce SnipRec, a novel CRS that enhances dialogues and recommendations by extracting diverse expressions and preferences from user-generated content (UGC) like customer reviews. Using large language models, SnipRec maps user responses and UGC to concise snippets, which are used to generate clarification questions and retrieve relevant items. Our approach eliminates the need for domain-specific training, making it adaptable to new domains and effective without prior knowledge of user preferences. Extensive experiments on the Yelp dataset demonstrate the effectiveness of snippet-based representations against document and sentence-based representations. Additionally, SnipRec is able to improve Hits@10 by 0.25 over the course of five conversational turns, underscoring the efficiency of SnipRec in capturing user preferences through multi-turn conversations.

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)
• Leveraging Knowledge Graph Embedding for Effective Conversational Recommendation [4.079573593766921]
  We propose a knowledge graph based conversational recommender system (referred as KG-CRS) Specifically, we first integrate the user-item graph and item-attribute graph into a dynamic graph, dynamically changing during the dialogue process by removing negative items or attributes. We then learn informative embedding of users, items, and attributes by also considering propagation through neighbors on the graph.
  arXiv  Detail & Related papers  (2024-08-02T15:38:55Z)
• MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models [51.65439315425421]
  We propose a Memory-enhanced Conversational Recommender System Framework with Large Language Models (dubbed MemoCRS) User-specific memory is tailored to each user for their personalized interests. The general memory, encapsulating collaborative knowledge and reasoning guidelines, can provide shared knowledge for users.
  arXiv  Detail & Related papers  (2024-07-06T04:57:25Z)
• Parameter-Efficient Conversational Recommender System as a Language Processing Task [52.47087212618396]
  Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation. Prior work often utilizes external knowledge graphs for items' semantic information, a language model for dialogue generation, and a recommendation module for ranking relevant items. In this paper, we represent items in natural language and formulate CRS as a natural language processing task.
  arXiv  Detail & Related papers  (2024-01-25T14:07:34Z)
• Conversational Recommendation as Retrieval: A Simple, Strong Baseline [4.737923227003888]
  Conversational recommendation systems (CRS) aim to recommend suitable items to users through natural language conversation. Most CRS approaches do not effectively utilize the signal provided by these conversations. We propose an alternative information retrieval (IR)-styled approach to the CRS item recommendation task.
  arXiv  Detail & Related papers  (2023-05-23T06:21:31Z)
• COLA: Improving Conversational Recommender Systems by Collaborative Augmentation [9.99763097964222]
  We propose a collaborative augmentation (COLA) method to improve both item representation learning and user preference modeling. We construct an interactive user-item graph from all conversations, which augments item representations with user-aware information. To improve user preference modeling, we retrieve similar conversations from the training corpus, where the involved items and attributes that reflect the user's potential interests are used to augment the user representation.
  arXiv  Detail & Related papers  (2022-12-15T12:37:28Z)
• User-Centric Conversational Recommendation with Multi-Aspect User Modeling [47.310579802092384]
  We propose a User-Centric Conversational Recommendation (UCCR) model, which returns to the essence of user preference learning in CRS tasks. A multi-view preference mapper is conducted to learn the intrinsic correlations among different views in current and historical sessions. The learned multi-aspect multi-view user preferences are then used for the recommendation and dialogue generation.
  arXiv  Detail & Related papers  (2022-04-20T07:08:46Z)
• Learning to Ask Appropriate Questions in Conversational Recommendation [49.31942688227828]
  We propose the Knowledge-Based Question Generation System (KBQG), a novel framework for conversational recommendation. KBQG models a user's preference in a finer granularity by identifying the most relevant relations from a structured knowledge graph. Finially, accurate recommendations can be generated in fewer conversational turns.
  arXiv  Detail & Related papers  (2021-05-11T03:58:10Z)
• Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion [77.21442487537139]
  Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference.
  arXiv  Detail & Related papers  (2020-07-08T11:14:23Z)

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.