A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles

• URL: http://arxiv.org/abs/2102.08322v1
• Date: Tue, 16 Feb 2021 18:05:54 GMT
• Title: A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles
• Authors: Jiahuan Pei, Pengjie Ren, Maarten de Rijke
• Abstract summary: We study personalized Task-oriented Dialogue Systems without assuming that user profiles are complete. We propose a Cooperative Memory Network (CoMemNN) that has a novel mechanism to gradually enrich user profiles. CoMemNN is able to enrich user profiles effectively, which results in an improvement of 3.06% in terms of response selection accuracy.
• Score: 55.951126447217526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is increasing interest in developing personalized Task-oriented Dialogue Systems (TDSs). Previous work on personalized TDSs often assumes that complete user profiles are available for most or even all users. This is unrealistic because (1) not everyone is willing to expose their profiles due to privacy concerns; and (2) rich user profiles may involve a large number of attributes (e.g., gender, age, tastes, . . .). In this paper, we study personalized TDSs without assuming that user profiles are complete. We propose a Cooperative Memory Network (CoMemNN) that has a novel mechanism to gradually enrich user profiles as dialogues progress and to simultaneously improve response selection based on the enriched profiles. CoMemNN consists of two core modules: User Profile Enrichment (UPE) and Dialogue Response Selection (DRS). The former enriches incomplete user profiles by utilizing collaborative information from neighbor users as well as current dialogues. The latter uses the enriched profiles to update the current user query so as to encode more useful information, based on which a personalized response to a user request is selected. We conduct extensive experiments on the personalized bAbI dialogue benchmark datasets. We find that CoMemNN is able to enrich user profiles effectively, which results in an improvement of 3.06% in terms of response selection accuracy compared to state-of-the-art methods. We also test the robustness of CoMemNN against incompleteness of user profiles by randomly discarding attribute values from user profiles. Even when discarding 50% of the attribute values, CoMemNN is able to match the performance of the best performing baseline without discarding user profiles, showing the robustness of CoMemNN.

Related papers

• Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training [8.272412404173954]
  We present AdvXMultVAE which aims to unlearn multiple protected attributes simultaneously to improve fairness across demographic user groups. Our experiments on two datasets, LFM-2b-100k and Ml-1m, show that our approach can yield better results than its singular removal counterparts.
  arXiv  Detail & Related papers  (2024-10-28T12:36:00Z)
• Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis [69.37718774071793]
  This paper introduces novel information-theoretic measures for understanding recommender systems. We evaluate 7 recommendation algorithms across 9 datasets, revealing the relationships between our measures and standard performance metrics.
  arXiv  Detail & Related papers  (2024-10-03T13:02:07Z)
• Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach [49.63614966954833]
  Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy. This paper proposes a novel personalized FedCF method by preserving users' personalized information into a latent variable and a neural model simultaneously. To effectively train the proposed framework, we model the problem as a specialized Variational AutoEncoder (VAE) task by integrating user interaction vector reconstruction with missing value prediction.
  arXiv  Detail & Related papers  (2024-08-16T05:49:14Z)
• Prompt Tuning as User Inherent Profile Inference Machine [53.78398656789463]
  We propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. A profile quantization codebook bridges the modality gap by profile embeddings into collaborative IDs. Experiments on four public datasets show that UserIP-Tuning outperforms state-of-the-art recommendation algorithms.
  arXiv  Detail & Related papers  (2024-08-13T02:25:46Z)
• Understanding the Role of User Profile in the Personalization of Large Language Models [19.74964898049076]
  This study first confirms that the effectiveness of user profiles is primarily due to personalization information rather than semantic information. Within the user profile, it is the historical personalized response produced or approved by users that plays a pivotal role in personalizing LLMs. Our findings reveal the role of user profiles for the personalization of LLMs, and showcase how incorporating user profiles impacts performance.
  arXiv  Detail & Related papers  (2024-06-22T14:32:35Z)
• Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement [79.2400720115588]
  We introduce Persona-DB, a simple yet effective framework consisting of a hierarchical construction process to improve generalization across task contexts. In the evaluation of response prediction, Persona-DB demonstrates superior context efficiency in maintaining accuracy with a significantly reduced retrieval size. Our experiments also indicate a marked improvement of over 10% under cold-start scenarios, when users have extremely sparse data.
  arXiv  Detail & Related papers  (2024-02-16T20:20:43Z)
• FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix Factorization [1.9181612035055007]
  We propose a novel algorithm for predicting user attributes without requiring user matching. Our approach involves training deep matrix factorization models on different clients and sharing only attribute item vectors. This allows us to predict user attributes without sharing the user vectors themselves.
  arXiv  Detail & Related papers  (2023-12-24T06:49:00Z)
• Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function [19.652303125864204]
  We propose a novel framework, P-ToD, to personalize task-oriented dialog systems. P-ToD uses a pre-trained GPT-2 as a backbone model and works in three phases. Our novel reward function can quantify the quality of the generated responses even for unseen profiles.
  arXiv  Detail & Related papers  (2023-03-24T04:33:40Z)
• Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot [29.053654530024083]
  IMPChat aims to learn an implicit user profile through modeling user's personalized language style and personalized preferences separately. To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses. We match each response candidate with the personalized language style and personalized preference, respectively, and fuse the two matching signals to determine the final ranking score.
  arXiv  Detail & Related papers  (2021-08-18T02:07:28Z)
• A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation [52.743311026230714]
  Persona Exploration and Exploitation (PEE) is able to extend the predefined user persona description with semantically correlated content. PEE consists of two main modules: persona exploration and persona exploitation. Our approach outperforms state-of-the-art baselines in terms of both automatic and human evaluations.
  arXiv  Detail & Related papers  (2020-02-06T08:24:33Z)

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.