|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/basic-recommender-systems
课程评论：没有评论
课程名称：基本推荐系统 课程概述：本课程介绍了推荐系统的主要方法，涵盖了协作过滤和基于内容的方法，并包括用于提供推荐的最重要算法。您将学习这些算法如何工作、如何使用以及如何评估它们，同时指出不同推荐系统选项的优缺点。完成此课程后，您将能够描述不同应用领域推荐系统的要求和目标。您将了解如何根据输入数据、内部工作机制和目标来区分推荐系统。您还将掌握衡量推荐系统质量的工具，并能通过设计新算法逐步改进推荐系统。同时，您将学习如何为新的应用领域设计定制的推荐系统，考虑到身份、隐私和操控等社会和伦理问题。 提供经济实惠、个性化且高质量的推荐一直是一项挑战！本课程还利用了两个重要的EIT跨学科学习成果（OLO），与创造力和创新技能相关。在设计新的推荐系统时，您需要超越界限，思考如何提高预测质量。您还应能够运用知识、创意和技术，创造新的或显著改进的推荐工具，以支持不同创新场景中的决策过程和策略，从而提升生活质量。 课程大纲： 1. 基本概念：回顾推荐系统的基本概念，以分类和分析与特定输入数据集相关的不同算法系列。您将能够根据可用数据、需求和目标选择最合适的算法类型。 2. 推荐系统评估：学习如何定义和衡量推荐系统的质量，回顾为此目的可用的不同指标。您将能够识别所需的正确评估活动，以衡量特定推荐系统的质量。 3. 基于内容的过滤：分析基于内容的推荐技术。这些算法推荐与用户以前喜欢的项目相似的项目。我们将回顾不同的相似性函数，并帮助您选择适合您系统的函数。您还将了解如何通过规范化和调整每个属性在项目内容矩阵（ICM）中的重要性来提高基于内容技术的质量。 4. 协作过滤：研究使用用户评分矩阵（URM）作为主要输入数据的协作过滤技术，这描述了用户与项目之间的互动。您将学习如何构建非个性化推荐系统，以及如何规范化URM，从而提供更好的推荐。最终，您能够选择最合适的相似性函数和计算相似性的最佳方法，克服与显式评分相关的问题。
Name:BASIC CONCEPTS
Description:In this first module, we'll review the basic concepts for recommender systems in order to classify and analyse different families of algorithms, related to specific set of input data. At the end, you’ll be able to choose the most suitable type of algorithm based on the data available, your needs and goals. Conversely, you'll know how to select the input data based on the algorithm you want to use.
Name:EVALUATION OF RECOMMENDER SYSTEMS
Description:In this second module, we'll learn how to define and measure the quality of a recommender system. We'll review different metrics that can be used to measure for this purpose. At the end of the module you'll be able to identify the correct evaluation activities required to measure the quality of a given recommender system, based on goals and needs.
Name:CONTENT-BASED FILTERING
Description:In this module we’ll analyse content-based recommender techniques. These algorithms recommend items similar to the ones a user liked in the past. We’ll review different similarity functions and you’ll then be able to choose the more suitable one for your system. The main input is the Item-Content Matrix (ICM) which describes all the attributes for each item. We’ll see how we can improve the quality of content-based techniques, by normalising and tuning the importance of each attribute in the ICM: you’ll be able to use some specific tuning strategies in order to obtain the best quality recommendations from your system. So, at the end of this module, you’ll know how to build a content-based recommender system, how to clean and normalize your input data.
Name:COLLABORATIVE FILTERING
Description:In this module we’ll study collaborative filtering techniques, which use the User Rating Matrix (URM) as the main input data, describing the interaction between users and items. We’ll learn how to build non-personalised recommender systems and how to normalise the URM, in order to provide better recommendations. At the end of the module you’ll be able to select the most appropriate similarity function and the most suitable way to compute similarity, overcoming issues related to explicit ratings.
This course introduces you to the leading approaches in recommender systems. The techniques described touch both collaborative and content-based approaches and include the most important algorithms used to provide recommendations. You'll learn how they work, how to use and how to evaluate them, pointing out benefits and limits of different recommender system alternatives. After completing this course, you'll be able to describe the requirements and objectives of recommender systems based on different application domains. You'll know how to distinguish recommender systems according to their input data, their internal working mechanisms, and their goals. You’ll have the tools to measure the quality of a recommender system and to incrementally improve it with the design of new algorithms. You'll learn as well how to design recommender systems tailored for new application domains, also considering surrounding social and ethical issues such as identity, privacy, and manipulation. Providing affordable, personalised and high-quality recommendations is always a challenge! This course also leverages two important EIT Overarching Learning Outcomes (OLOs), related to creativity and innovation skills. In trying to design a new recommender system you need to think beyond boundaries and try to figure out how you can improve the quality of the predictions. You should also be able to use knowledge, ideas and technology to create new or significantly improved recommendation tools to support choice-making processes and strategies in different and innovative scenarios, for a better quality of life.
