论文标题
探索使用时间依赖的跨网络信息进行个性化建议
Exploring the use of Time-Dependent Cross-Network Information for Personalized Recommendations
论文作者
论文摘要
在线应用程序中，信息的压倒性和信息的复杂性使用户找到感兴趣的信息至关重要。但是，在现实世界应用中共存的两个主要局限性（1）不完整的用户配置文件以及（2）用户偏好的动态性质继续在及时性，准确性，多样性和新颖性等方面降低推荐质量。为了解决单个解决方案中上述两个限制，我们提出了一种新颖的跨网络时间意识到的建议解决方案。该解决方案首先通过从多个源网络中汇总用户偏好来了解目标网络中的历史用户模型。其次，学会了用户级别的潜在因素，以从历史模型中开发当前的用户模型并进行及时的建议。我们通过使用来自Twitter源网络的辅助信息来改善YouTube目标网络的建议来说明我们的解决方案。在不同时间粒度下使用多个时间意识和跨网络基线进行的实验表明，所提出的解决方案在准确性，新颖性和多样性方面取得了卓越的性能。
The overwhelming volume and complexity of information in online applications make recommendation essential for users to find information of interest. However, two major limitations that coexist in real world applications (1) incomplete user profiles, and (2) the dynamic nature of user preferences continue to degrade recommender quality in aspects such as timeliness, accuracy, diversity and novelty. To address both the above limitations in a single solution, we propose a novel cross-network time aware recommender solution. The solution first learns historical user models in the target network by aggregating user preferences from multiple source networks. Second, user level time aware latent factors are learnt to develop current user models from the historical models and conduct timely recommendations. We illustrate our solution by using auxiliary information from the Twitter source network to improve recommendations for the YouTube target network. Experiments conducted using multiple time aware and cross-network baselines under different time granularities show that the proposed solution achieves superior performance in terms of accuracy, novelty and diversity.
