Recommendation with User Active Disclosing Willingness

• URL: http://arxiv.org/abs/2211.01155v1
• Date: Tue, 25 Oct 2022 04:43:40 GMT
• Title: Recommendation with User Active Disclosing Willingness
• Authors: Lei Wang, Xu Chen, Quanyu Dai, Zhenhua Dong
• Abstract summary: We study a novel recommendation paradigm, where the users are allowed to indicate their "willingness" on disclosing different behaviors. We conduct extensive experiments to demonstrate the effectiveness of our model on balancing the recommendation quality and user disclosing willingness.
• Score: 20.306413327597603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender system has been deployed in a large amount of real-world applications, profoundly influencing people's daily life and production.Traditional recommender models mostly collect as comprehensive as possible user behaviors for accurate preference estimation. However, considering the privacy, preference shaping and other issues, the users may not want to disclose all their behaviors for training the model. In this paper, we study a novel recommendation paradigm, where the users are allowed to indicate their "willingness" on disclosing different behaviors, and the models are optimized by trading-off the recommendation quality as well as the violation of the user "willingness". More specifically, we formulate the recommendation problem as a multiplayer game, where the action is a selection vector representing whether the items are involved into the model training. For efficiently solving this game, we design a tailored algorithm based on influence function to lower the time cost for recommendation quality exploration, and also extend it with multiple anchor selection vectors.We conduct extensive experiments to demonstrate the effectiveness of our model on balancing the recommendation quality and user disclosing willingness.

Related papers

• Learning Recommender Systems with Soft Target: A Decoupled Perspective [49.83787742587449]
  We propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels. We present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors.
  arXiv  Detail & Related papers  (2024-10-09T04:20:15Z)
• Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement [5.734747179463411]
  We propose a Contrastive Learning sequential recommendation method based on Multi-Intention Disentanglement (MIDCL) In our work, intentions are recognized as dynamic and diverse, and user behaviors are often driven by current multi-intentions. We propose two types of contrastive learning paradigms for finding the most relevant user's interactive intention, and maximizing the mutual information of positive sample pairs.
  arXiv  Detail & Related papers  (2024-04-28T15:13:36Z)
• Interactive Garment Recommendation with User in the Loop [77.35411131350833]
  We propose to build a user profile on the fly by integrating user reactions as we recommend complementary items to compose an outfit. We present a reinforcement learning agent capable of suggesting appropriate garments and ingesting user feedback to improve its recommendations.
  arXiv  Detail & Related papers  (2024-02-18T16:01:28Z)
• Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders [13.762960304406016]
  We introduce explicit and implicit negative user feedback into the training objective of sequential recommenders. We demonstrate the effectiveness of this approach using live experiments on a large-scale industrial recommender system.
  arXiv  Detail & Related papers  (2023-08-23T17:16:07Z)
• Reusable Self-Attention-based Recommender System for Fashion [1.978884131103313]
  We present a reusable Attention-based Fashion Recommendation Algorithm (AFRA) We leverage temporal and contextual information to address both short and long-term customer preferences. We show its effectiveness on outfit recommendation use cases, in particular: 1) personalized ranked feed; 2) outfit recommendations by style; 3) similar item recommendation and 4) in-session recommendations inspired by most recent customer actions.
  arXiv  Detail & Related papers  (2022-11-29T16:47:20Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)
• Generative Inverse Deep Reinforcement Learning for Online Recommendation [62.09946317831129]
  We propose a novel inverse reinforcement learning approach, namely InvRec, for online recommendation. InvRec extracts the reward function from user's behaviors automatically, for online recommendation.
  arXiv  Detail & Related papers  (2020-11-04T12:12:25Z)
• Latent Unexpected Recommendations [89.2011481379093]
  We propose to model unexpectedness in the latent space of user and item embeddings, which allows to capture hidden and complex relations between new recommendations and historic purchases. In addition, we develop a novel Latent Closure (LC) method to construct hybrid utility function and provide unexpected recommendations based on the proposed model.
  arXiv  Detail & Related papers  (2020-07-27T02:39:30Z)
• Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty [17.258674767363345]
  Most existing embedding based recommendation models use embeddings corresponding to a single fixed point in low-dimensional space. We propose a unified deep recommendation framework employing Gaussian embeddings, which are proven adaptive to uncertain preferences. Our framework adopts Monte-Carlo sampling and convolutional neural networks to compute the correlation between the objective user and the candidate item.
  arXiv  Detail & Related papers  (2020-06-19T02:10:38Z)

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.