Introducing Context Information in Lifelong Sequential Modeling using Temporal Convolutional Networks

• URL: http://arxiv.org/abs/2502.12634v1
• Date: Tue, 18 Feb 2025 08:24:53 GMT
• Title: Introducing Context Information in Lifelong Sequential Modeling using Temporal Convolutional Networks
• Authors: Ting Guo, Zhaoyang Yang, Qinsong Zeng, Ming Chen,
• Abstract summary: We introduce a novel network which employs the Temporal Convolutional Network (TCN) to generate context-aware representations for each item throughout the lifelong sequence.<n>We also incorporate a lightweight sub-network to create convolution filters based on users' basic profile features.<n>The findings indicate that the proposed network surpasses existing methods in terms of prediction accuracy and online performance metrics.
• Score: 4.561273938467592
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The importance of lifelong sequential modeling (LSM) is growing in the realm of social media recommendation systems. A key component in this process is the attention module, which derives interest representations with respect to candidate items from the sequence. Typically, attention modules function in a point-wise fashion, concentrating only on the relevance of individual items in the sequence to the candidate item. However, the context information in the neighboring items that is useful for more accurately evaluating the significance of each item has not been taken into account. In this study, we introduce a novel network which employs the Temporal Convolutional Network (TCN) to generate context-aware representations for each item throughout the lifelong sequence. These improved representations are then utilized in the attention module to produce context-aware interest representations. Expanding on this TCN framework, we present a enhancement module which includes multiple TCN layers and their respective attention modules to capture interest representations across different context scopes. Additionally, we also incorporate a lightweight sub-network to create convolution filters based on users' basic profile features. These personalized filters are then applied in the TCN layers instead of the original global filters to produce more user-specific representations. We performed experiments on both a public dataset and a proprietary dataset. The findings indicate that the proposed network surpasses existing methods in terms of prediction accuracy and online performance metrics.

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