Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models

• URL: http://arxiv.org/abs/2302.08128v1
• Date: Thu, 16 Feb 2023 07:38:51 GMT
• Title: Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models
• Authors: Xuanji Xiao, Ziyu He
• Abstract summary: We propose a novel neighbor enhancement structure to help train the representation of the target user or item. Experiments on the well-known public dataset MovieLens 1M demonstrate the efficiency of the method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rank models play a key role in industrial recommender systems, advertising, and search engines. Existing works utilize semantic tags and user-item interaction behaviors, e.g., clicks, views, etc., to predict the user interest and the item hidden representation for estimating the user-item preference score. However, these behavior-tag-based models encounter great challenges and reduced effectiveness when user-item interaction activities are insufficient, which we called "the long-tail ranking problem". Existing rank models ignore this problem, but its common and important because any user or item can be long-tailed once they are not consistently active for a short period. In this paper, we propose a novel neighbor enhancement structure to help train the representation of the target user or item. It takes advantage of similar neighbors (static or dynamic similarity) with multi-level attention operations balancing the weights of different neighbors. Experiments on the well-known public dataset MovieLens 1M demonstrate the efficiency of the method over the baseline behavior-tag-based model with an absolute CTR AUC gain of 0.0259 on the long-tail user dataset.

Related papers

• Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction [68.90783662117936]
  Click-through Rate (CTR) prediction is crucial for online personalization platforms. Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction. We propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN)
  arXiv  Detail & Related papers  (2024-11-22T15:29:05Z)
• Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction [32.35797628133921]
  Deep Group Interest Network (DGIN) is an end-to-end method to model the user's entire behavior history. DGIN grouping reduces the behavior length significantly, from O(104) to O(102) We identify a subset of behaviors that share the same item_id with the candidate item from the lifelong behavior sequence.
  arXiv  Detail & Related papers  (2023-11-15T06:36:11Z)
• Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce [21.7796124109]
  We propose Attention Weighted Mixture of Experts (AW-MoE) with contrastive learning for personalized ranking. AW-MoE has been successfully deployed in the JD e-commerce search engine.
  arXiv  Detail & Related papers  (2023-06-08T07:59:08Z)
• Multi-Behavior Sequential Recommendation with Temporal Graph Transformer [66.10169268762014]
  We tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns. We propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns.
  arXiv  Detail & Related papers  (2022-06-06T15:42:54Z)
• IA-GCN: Interactive Graph Convolutional Network for Recommendation [13.207235494649343]
  Graph Convolutional Network (GCN) has become a novel state-of-the-art for Collaborative Filtering (CF) based Recommender Systems (RS) We build bilateral interactive guidance between each user-item pair and propose a new model named IA-GCN (short for InterActive GCN) Our model is built on top of LightGCN, a state-of-the-art GCN model for CF, and can be combined with various GCN-based CF architectures in an end-to-end fashion.
  arXiv  Detail & Related papers  (2022-04-08T03:38:09Z)
• Sequential Search with Off-Policy Reinforcement Learning [48.88165680363482]
  We propose a highly scalable hybrid learning model that consists of an RNN learning framework and an attention model. As a novel optimization step, we fit multiple short user sequences in a single RNN pass within a training batch, by solving a greedy knapsack problem on the fly. We also explore the use of off-policy reinforcement learning in multi-session personalized search ranking.
  arXiv  Detail & Related papers  (2022-02-01T06:52:40Z)
• 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)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations [3.286961611175469]
  We propose a new deep learning-based sequential recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation. It realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions.
  arXiv  Detail & Related papers  (2021-07-12T07:34:20Z)
• Dynamic Graph Collaborative Filtering [64.87765663208927]
  Dynamic recommendation is essential for recommender systems to provide real-time predictions based on sequential data. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.
  arXiv  Detail & Related papers  (2021-01-08T04:16:24Z)

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.