Multi-purpose Recommender Platform using Perceiver IO

Ali Cevahir, Kentaro Kanada
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引用次数: 0

Abstract

Web services usually require many different types of recommender systems using large amount of user log and content data, in order to provide personalized content to their customers. Different recommenders may share the same customer-base or cross-use models/data. It is challenging to design different models for each recommendation task. In this work, we propose a general-purpose framework for various recommendation tasks based on Perceiver IO model. Perceiver lOis a general ma-chine learning architecture based on transformer-style attention modules, which helps eliminating feature engineering for various tasks. Different type of recommenders can be developed with minimal modifications and models can be transferred among dif- ferent tasks. Our experiments with a variety of recommendation scenarios confirm that our framework is able to handle those tasks while achieving state-of-the-art accuracy.
使用percepver IO的多用途推荐平台
Web服务通常需要使用大量用户日志和内容数据的许多不同类型的推荐系统,以便向客户提供个性化的内容。不同的推荐人可能共享相同的客户基础或交叉使用的模型/数据。为每个推荐任务设计不同的模型是一个挑战。在这项工作中,我们提出了一个基于感知器IO模型的各种推荐任务的通用框架。感知器lOis是一种基于变压器式注意力模块的通用机器学习架构,有助于消除各种任务的特征工程。不同类型的推荐可以用最小的修改开发,模型可以在不同的任务之间转移。我们对各种推荐场景的实验证实,我们的框架能够处理这些任务,同时达到最先进的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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