论文标题
迈向全面的推荐系统：基于隐式跨网络数据的列表排名的时间感知统一委员会
Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data
论文作者
论文摘要
Web应用程序中的大量信息为用户和应用程序提供了建议。尽管现有推荐系统具有有效性，但我们发现两个主要限制降低了它们的整体性能：（1）通过考虑用户偏好的动态性质，无法为新用户和现有用户提供及时的建议，并且（2）在使用隐式反馈时未对排名任务进行完全优化。因此，我们提出了一种新型的基于深度学习的统一跨网络解决方案，以减轻冷启动和数据稀疏问题，并为新用户和现有用户提供及时的建议。Furthermore，我们将隐式反馈下的排名问题视为分类任务，并提出一个通用的个性化listiswise list Oightization listization Optimization Optimization Optiration Criteriation，以有效地排名项目列表的列表。我们使用Twitter辅助信息说明了我们的跨网络模型，以在YouTube目标网络上提出建议。与多个时间意识和跨网络基线的广泛比较表明，在准确性，新颖性和多样性方面，所提出的解决方案优越。此外，在流行的Movielens数据集上进行的实验表明，所提出的列表排名方法优于现有的最新排名技术。
The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing users by considering the dynamic nature of user preferences, and (2) not fully optimized for the ranking task when using implicit feedback. Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users.Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items. We illustrate our cross-network model using Twitter auxiliary information for recommendations on YouTube target network. Extensive comparisons against multiple time aware and cross-network base-lines show that the proposed solution is superior in terms of accuracy, novelty and diversity. Furthermore, experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques.
