Cloud-based Personal Information Harvesting and Recommendation Service

基于云的个人信息收集和推荐服务

• 批准号: RGPIN-2020-04760
• 负责人: Ding, Chen(Cherie)
• 金额: $ 2.55万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717345
• 关键词: Cloud; based; Personal; Information; Harvesting

Recommender systems have become useful tools for helping users deal with information overload problems. However, most of these systems today are built by the content or product providers (e.g., Amazon, Netflix) to recommend their own content or product to users. In this proposed research, we will design an information harvesting and recommendation system to facilitate individual users' online information-seeking and decision-making process. We plan to implement it as a cloud-based service. Compared to traditional systems, developing a user responsive recommender system is more complex and presents unique challenges, including: 1) finding data that is useful for building recommender systems, for example, social media data is rich in users' opinions on various items, nonetheless, identifying the most suitable subset for a user's need may be challenging; 2) integrating multiple data sources and transferring learned knowledge from these sources for effective recommendation; 3) identifying user decision criteria (beyond commonly considered predicted ratings) when choosing an item, and building a multi-criteria recommender system; 4) continuously monitoring user intent, need and emotion, and recommending items according to these psychological factors. Our objective is to build a personal recommendation service that can identify the right data sources for the target user and the item to be recommended, and make recommendations based on data collected from these sources as well as user decision criteria and intent. To begin with, we will select one or two use cases, such as a movie recommender service, an API (Application Programming Interface) recommender service, or a service recommending research papers to PhD students. We will collect data from multiple sources containing ratings, reviews and item features, including social media sites. We will then propose an effective integration approach. From sources with user interaction data, we will identify user decision criteria as well as intent and motive behind the information need. A multi-criteria recommender service will then be implemented. To the best of our knowledge, once successfully implemented, our personal recommender service will be the first such service encompassing all the mentioned characteristics. It could help users complete their daily tasks more efficiently. The consideration of psychological factors could help recommending items that are beneficial to a user's well-being. Our research agenda aligns with Canada's national strategy on AI (artificial intelligence) since we are exploring the use of different machine learning (a type of AI algorithms) models in building our recommender service. We will also train a number of HQPs to gain a deep understanding on topics such as recommender systems, social network analysis, machine learning and multi-criteria decision making. In addition, they will gain hands-on experiences in building cloud-based applications and personal assistant service.

推荐系统已经成为帮助用户处理信息过载问题的有用工具。然而，今天的这些系统中的大多数是由内容或产品提供商（例如，亚马逊，Netflix）向用户推荐自己的内容或产品。在本研究中，我们将设计一个资讯收集与推荐系统，以协助个人使用者的线上资讯搜寻与决策过程。我们计划将其作为基于云的服务来实施。与传统系统相比，开发用户响应式推荐系统更加复杂，并且提出了独特的挑战，包括：1）找到对构建推荐系统有用的数据，例如，社交媒体数据富含用户对各种项目的意见，然而，识别用户需求的最合适子集可能是具有挑战性的; 2）集成多个数据源并从这些源中转移所学习的知识以进行有效的推荐; 3）识别用户决策标准（超出通常认为的预测评级）时选择一个项目，并建立一个多标准推荐系统; 4）持续监测用户意图、需求和情感，并根据这些心理因素推荐商品。我们的目标是构建一个个人推荐服务，可以为目标用户和要推荐的项目识别正确的数据源，并根据从这些来源收集的数据以及用户决策标准和意图提出推荐。开始，我们将选择一个或两个用例，例如电影推荐服务、API（应用程序编程接口）推荐服务或向博士生推荐研究论文的服务。我们将从多个来源收集数据，包括评级，评论和项目功能，包括社交媒体网站。然后，我们将提出一个有效的整合方法。从用户交互数据的来源，我们将确定用户决策标准以及信息需求背后的意图和动机。一个多标准的推荐服务，然后将实施。据我们所知，一旦成功实施，我们的个人推荐服务将是第一个包含所有上述特征的此类服务。它可以帮助用户更有效地完成日常任务。心理因素的考虑可以帮助推荐对用户的健康有益的项目。我们的研究议程与加拿大的人工智能（AI）国家战略保持一致，因为我们正在探索使用不同的机器学习（一种人工智能算法）模型来构建我们的推荐服务。我们还将培训一些HQP，以深入了解推荐系统，社交网络分析，机器学习和多标准决策等主题。此外，他们将获得构建基于云的应用程序和个人助理服务的实践经验。