Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling

• URL: http://arxiv.org/abs/2106.07356v1
• Date: Fri, 4 Jun 2021 07:52:52 GMT
• Title: Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
• Authors: Zhenhui Xu, Meng Zhao, Liqun Liu, Xiaopeng Zhang and Bifeng Zhang
• Abstract summary: deep learning is widely used to mine expressive tags to describe users' preferences from their historical actions. Traditional solutions usually introduce multiple independent Two-Tower models to mine tags from different actions. This paper introduces a novel multi-task model called Mixture of Virtual- Kernel Experts (MVKE) to learn multiple topic-related user preferences.
• Score: 9.639497198579257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many industrial applications like online advertising and recommendation systems, diverse and accurate user profiles can greatly help improve personalization. For building user profiles, deep learning is widely used to mine expressive tags to describe users' preferences from their historical actions. For example, tags mined from users' click-action history can represent the categories of ads that users are interested in, and they are likely to continue being clicked in the future. Traditional solutions usually introduce multiple independent Two-Tower models to mine tags from different actions, e.g., click, conversion. However, the models cannot learn complementarily and support effective training for data-sparse actions. Besides, limited by the lack of information fusion between the two towers, the model learning is insufficient to represent users' preferences on various topics well. This paper introduces a novel multi-task model called Mixture of Virtual-Kernel Experts (MVKE) to learn multiple topic-related user preferences based on different actions unitedly. In MVKE, we propose a concept of Virtual-Kernel Expert, which focuses on modeling one particular facet of the user's preference, and all of them learn coordinately. Besides, the gate-based structure used in MVKE builds an information fusion bridge between two towers, improving the model's capability much and maintaining high efficiency. We apply the model in Tencent Advertising System, where both online and offline evaluations show that our method has a significant improvement compared with the existing ones and brings about an obvious lift to actual advertising revenue.

Related papers

• GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation [55.769720670731516]
  GaVaMoE is a novel framework for explainable recommendation. It generates tailored explanations for specific user types and preferences. It exhibits robust performance in scenarios with sparse user-item interactions.
  arXiv  Detail & Related papers  (2024-10-15T17:59:30Z)
• CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems [0.0]
  We introduce CAM2, a conformity-aware multi-task ranking model to serve relevant items to users on one of the largest industrial recommendation platforms. We show through online experiments that the CAM2 model results in a significant 0.50% increase in aggregated user engagement.
  arXiv  Detail & Related papers  (2023-04-17T19:00:55Z)
• Knowledge Enhancement for Multi-Behavior Contrastive Recommendation [39.50243004656453]
  We propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework. In this work, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement. In the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect.
  arXiv  Detail & Related papers  (2023-01-13T06:24:33Z)
• 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)
• Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation [46.04494053005958]
  We seek an alternative framework that is light and effective through self-supervised learning across different sources of information. We use a self-supervision signal to pair users with the auxiliary information associated with the items they have interacted with before. We show that our method can achieve better performance while requiring less training time.
  arXiv  Detail & Related papers  (2022-11-11T17:43:19Z)
• Contrastive Meta Learning with Behavior Multiplicity for Recommendation [42.15990960863924]
  A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms. We propose Contrastive Meta Learning (CML) to maintain dedicated cross-type behavior dependency for different users. Our method consistently outperforms various state-of-the-art recommendation methods.
  arXiv  Detail & Related papers  (2022-02-17T08:51:24Z)
• Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [56.12499090935242]
  This work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) to investigate multi-typed interactive patterns between users and items in recommender systems. KHGT is built upon a graph-structured neural architecture to capture type-specific behavior characteristics. We show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings.
  arXiv  Detail & Related papers  (2021-10-08T09:44:00Z)
• Modeling High-order Interactions across Multi-interests for Micro-video Reommendation [65.16624625748068]
  We propose a Self-over-Co Attention module to enhance user's interest representation. In particular, we first use co-attention to model correlation patterns across different levels and then use self-attention to model correlation patterns within a specific level.
  arXiv  Detail & Related papers  (2021-04-01T07:20:15Z)
• Exploiting Behavioral Consistence for Universal User Representation [11.290137806288191]
  We focus on developing universal user representation model. The obtained universal representations are expected to contain rich information. We propose Self-supervised User Modeling Network (SUMN) to encode behavior data into the universal representation.
  arXiv  Detail & Related papers  (2020-12-11T06:10:14Z)
• Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering [23.056754242935824]
  Attentive Multi-modal AutoRec tracks user preferences with multi-modal latent representations. We show that AMA is competitive with state-of-the-art models under the OC-CF setting.
  arXiv  Detail & Related papers  (2020-10-24T06:35:44Z)
• Explainable Recommender Systems via Resolving Learning Representations [57.24565012731325]
  Explanations could help improve user experience and discover system defects. We propose a novel explainable recommendation model through improving the transparency of the representation learning process.
  arXiv  Detail & Related papers  (2020-08-21T05:30:48Z)

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.