EAGER: Data Management Systems Support for Personalized Recommendation Applications

推荐系统帮助用户从一个相当大的搜索空间中识别有用和有趣的项目。推荐系统已经广泛应用于各种商业服务中。推荐系统利用用户的意见，以便为每个用户提取一组感兴趣的项目。该项目进行研究，开发必要的知识和构建软件基础设施，以支持个性化推荐应用程序的高效，可销售和可用的数据管理。推荐系统已经被广泛使用，对所有网络用户产生了广泛的影响，该项目旨在将个性化推荐应用推荐系统推向下一阶段，并将其范围扩大到新的应用。该项目通过分发一个免费的可移植的软件工件来增强研究基础设施。所有提出的想法都将在亚利桑那州立大学维护的一个开源的自动化感知数据库系统中实现。据设想，建议的系统将被世界各地的研究人员作为一种工具，用于评估他们的研究和交流新的模块相关的推荐系统。据设想，几个商业数据库系统将采用该项目的想法。该项目将有一个重要的教育部分。数据管理和推荐系统的研究人员将通过拟议的项目，通过课程创新以及讲习班和辅导进行培训。该项目解决了以下系统挑战，以支持推荐应用：（1）灵活性和可用性：用户应该能够使用适合应用需求的流行推荐算法以声明方式定义各种各样的推荐器。系统必须能够将推荐功能与其他数据属性/源集成，并能够并行执行推荐功能和其他数据访问操作。(2)效率和可扩展性：该系统预计将产生个性化的建议，以大量的用户同时在一个大的项目池。不幸的是，推荐模型不容易更新，因此它们需要定期重建。因此，模型会随着时间的推移而失去准确性，直到下一次重建过程。这在现代应用中是不可接受的（例如，社交媒体），其中新的项目和评级流入系统。为了应对这些挑战，该项目通过以下方式将推荐功能注入数据库系统的核心功能中：（a）索引查询器的集合以有效地回答ad-hoc推荐查询，（B）将推荐功能封装在与其他数据库操作符集成的可流水线操作符中，并设计包括推荐功能的查询优化技术。此外，由于训练推荐模型的常见操作是以张量的形式分解多关系用户，项目和属性数据，因此该建议开发了一个可扩展的并行化数据处理软件框架，该框架提供张量代数和关系代数运算的协同优化。该项目还利用数据库系统来支持上下文（例如，空间位置和社交网络）感知推荐。

A recommender system helps users to identify useful and interesting items from a considerably large search space. Recommender systems have been widely used in various commercial services. A recommender system exploits the users' opinions in order to extract a set of interesting items for each user. This project conducts research, develop requisite knowledge and build software infrastructure to support efficient, salable, and usable data management for personalized recommendation applications. Recommender systems have already been widely used with a strong broad impact on all web users and the project aims to take personalized recommendation applications recommender systems to its next stage and widening its scope to new applications. The project enhances the research infrastructure by distributing a free and portable software artifact. All proposed ideas will be realized inside an open-source recommendation-aware database system maintained at Arizona State University. It is envisioned that the proposed system will be used by researchers world wide as a vehicle for evaluating their research and exchanging new modules related to recommender systems. It is also envisioned that several commercial database systems will adopt the ideas from this project. The project will have a significant educational component. Researchers in both data management and recommender systems will be trained through the proposed project, through curricular innovations as well as workshops and tutorials. Students will be introduced to career pathways through their participations in research.The project tackles the following system challenges to support recommendation applications: (1) Flexibility and Usability: The user should be able to declaratively define a variety of recommenders using popular recommendation algorithms that fit the application needs. The system must be able to integrate the recommendation functionality with other data attributes/sources as well as performing the recommendation functionality and other data access operations side by side. (2) Efficiency and scalability: The system is expected to produce personalized recommendations to a high number of users concurrently over a large pool of items. Unfortunately, recommender models are not easily updatable, and hence they are rebuilt periodically. As a result, the model loses its accuracy over time till the next rebuild process. This is not acceptable in modern applications (e.g., social media) where new items and ratings are streaming into the system. To tackle these challenges, the project injects the recommendation functionality inside the core functionality of a database system by: (a) indexing the set of recommenders to efficiently answer of ad-hoc recommendation queries, (b) encapsulating the recommendation functionality inside a pipeline-able query operator that integrates well with other database operators, and designing query optimization techniques that include the recommendation functionality. Moreover, since a common operation to train recommendation models is to factorize multi-relational user, item, and attribute data, in the forms of tensors, this proposal develops a scalable parallelizable data processing software framework that provides co-optimization of tensor-algebraic and relational algebraic operations. The project also leverages database systems to support context (e.g., spatial location and social network)-aware recommendations.