User-Centric Conversational Recommendation with Multi-Aspect User Modeling

• URL: http://arxiv.org/abs/2204.09263v1
• Date: Wed, 20 Apr 2022 07:08:46 GMT
• Title: User-Centric Conversational Recommendation with Multi-Aspect User Modeling
• Authors: Shuokai Li, Ruobing Xie, Yongchun Zhu, Xiang Ao, Fuzhen Zhuang, Qing He
• Abstract summary: 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.
• Score: 47.310579802092384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational recommender systems (CRS) aim to provide highquality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understanding of the current session, ignoring other rich multi-aspect information of the central subjects (i.e., users) in recommendation. In this work, we highlight that the user's historical dialogue sessions and look-alike users are essential sources of user preferences besides the current dialogue session in CRS. To systematically model the multi-aspect information, we propose a User-Centric Conversational Recommendation (UCCR) model, which returns to the essence of user preference learning in CRS tasks. Specifically, we propose a historical session learner to capture users' multi-view preferences from knowledge, semantic, and consuming views as supplements to the current preference signals. A multi-view preference mapper is conducted to learn the intrinsic correlations among different views in current and historical sessions via self-supervised objectives. We also design a temporal look-alike user selector to understand users via their similar users. The learned multi-aspect multi-view user preferences are then used for the recommendation and dialogue generation. In experiments, we conduct comprehensive evaluations on both Chinese and English CRS datasets. The significant improvements over competitive models in both recommendation and dialogue generation verify the superiority of UCCR.

Related papers

• 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)
• 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)
• Improving Conversational Recommendation Systems via Counterfactual Data Simulation [73.4526400381668]
  Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. We propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs.
  arXiv  Detail & Related papers  (2023-06-05T12:48:56Z)
• Modeling Multiple User Interests using Hierarchical Knowledge for Conversational Recommender System [13.545276171601769]
  A conversational recommender system (CRS) is a practical application for item recommendation through natural language conversation. We propose to model such multiple user interests in CRS. We investigated its effects in experiments using the ReDial dataset and found that the proposed method can recommend a wider variety of items than that of the baseline CR-Walker.
  arXiv  Detail & Related papers  (2023-03-01T08:15:48Z)
• 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)
• Customized Conversational Recommender Systems [45.84713970070487]
  Conversational recommender systems (CRS) aim to capture user's current intentions and provide recommendations through real-time multi-turn conversational interactions. We propose a novel CRS model, coined Customized Conversational Recommender System ( CCRS), which customizes CRS model for users from three perspectives. To provide personalized recommendations, we extract user's current fine-grained intentions from dialogue context with the guidance of user's inherent preferences.
  arXiv  Detail & Related papers  (2022-06-30T09:45:36Z)
• 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)
• CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation [62.13413129518165]
  CR-Walker is a model that performs tree-structured reasoning on a knowledge graph. It generates informative dialog acts to guide language generation. Automatic and human evaluations show that CR-Walker can arrive at more accurate recommendation.
  arXiv  Detail & Related papers  (2020-10-20T14:53:22Z)
• A Survey on Conversational Recommender Systems [11.319431345375751]
  Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing.
  arXiv  Detail & Related papers  (2020-04-01T18:00:47Z)

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.