TALMUD:多领域的迁移学习

Orly Moreno, Bracha Shapira, L. Rokach, Guy Shani
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引用次数: 93

摘要

大多数协作推荐系统(RS)在单个领域(如电影、书籍等)中运行,并且能够根据仅在特定领域收集的历史使用数据提供推荐。跨领域推荐通过使用机器学习(ML)技术将知识从密集领域转移到稀疏目标领域来解决稀疏性问题。在本文中,我们提出了一种迁移学习技术,该技术从包含丰富数据的多个领域(例如,电影和音乐)中提取知识,并为稀疏的目标领域(例如,游戏)生成推荐。我们的方法学习了不同源域和目标域之间的相关性,而不需要域之间有重叠的用户。该模型集成了各个领域的适量知识,以丰富目标领域数据。在多个数据集上的实验表明,使用多个数据源和域间的相关性可以提高结果的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TALMUD: transfer learning for multiple domains
Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine Learning (ML) techniques to transfer knowledge from a dense domain into a sparse target domain. In this paper we propose a transfer learning technique that extracts knowledge from multiple domains containing rich data (e.g., movies and music) and generates recommendations for a sparse target domain (e.g., games). Our method learns the relatedness between the different source domains and the target domain, without requiring overlapping users between domains. The model integrates the appropriate amount of knowledge from each domain in order to enrich the target domain data. Experiments with several datasets reveal that, using multiple sources and the relatedness between domains improves accuracy of results.
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