Advanced Recommender Systems

所在平台: Coursera

课程主页: https://www.coursera.org/learn/advanced-recommender-systems

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课程简介

课程名称：高级推荐系统 概述：在本课程中，您将学习如何利用先进的机器学习技术构建更复杂的推荐系统。机器学习能够通过利用用户历史意见自动构建模型，从而提供更好的推荐和预测，而无需您过多考虑模型的细节。通过本课程，您将掌握如何管理混合信息以及如何结合不同的过滤技术，充分利用每种方法的优点。您将学会使用因式分解机，并相应地表示输入数据，设计出能够解决跨领域推荐问题的更复杂的推荐系统。此外，您还将了解在各类创新应用场景中提供推荐的最新趋势和挑战。 本课程强调两个重要的EIT数字教育总体学习成果（OLOs），与您的创造力和创新技能相关。在设计新的推荐系统时，您需要突破界限，思考如何提高结果的质量。您应能够运用知识、理念和技术创造新的或显著改进的推荐工具，以支持决策过程并解决复杂创新场景中的现实问题。 课程大纲： 1. 高级协作过滤（ADVANCED COLLABORATIVE FILTERING）：学习应用机器学习技术于协作过滤，编写能自动学习物品之间最佳相似度的算法，从而提供更匹配用户意见的推荐，并定义基于排名比较的新误差指标，用于设计学习排名算法。 2. 奇异值分解技术（SINGULAR VALUE DECOMPOSITION TECHNIQUES - SVD）：研究基于维度降低和矩阵分解的协作过滤新技术，探讨基于记忆的和基于模型的推荐系统的区别与局限，并学习选择正确数量的潜在特征，以提供个性化推荐并降低过拟合风险。 3. 混合和情境感知推荐系统（HYBRID AND CONTEXT AWARE RECOMMENDER SYSTEMS）：学习如何将两种或多种基础算法（如协作过滤和基于内容的技术）结合成混合推荐系统，以提高推荐质量，研究不同的混合策略，包括简单启发式和复杂机器学习方法。 4. 因式分解机（FACTORIZATION MACHINES）：介绍一种基于侧信息的高级协作过滤技术，了解在使用此技术时如何表示输入数据，利用同一个数学模型创建简单的矩阵分解算法或复杂的协作过滤算法，讨论基于因式分解机的算法的优缺点，掌握如何平衡不同类型输入信息，进行更好的预测。 5. 推荐系统挑战（RecSys Challenge，荣誉段）：这是一个实践性练习，提供“动手实践”的机会，以提升您在课程中所学的知识。该挑战使用在线商店的4个月交易数据，目标是发现用户将与哪个项目互动。虽然该挑战为可选，但完成后将获得荣誉证书。 通过本课程的学习，您将具备设计和实现高级推荐系统的能力，满足现实世界中的复杂需求。

课程大纲

Name:ADVANCED COLLABORATIVE FILTERING

Description:In this first module, we will see how to apply machine learning to collaborative filtering techniques. We will learn how to write an item-based collaborative algorithm which is able to automatically learn the best similarities between items, in order to provide improved recommendations that better match the user opinions predicted by the model with the true user opinions. We will also understand how to train collaborative filtering algorithms that minimize this gap. We will finally define a new error metric based on ranking comparisons, useful to design learning-to-rank algorithms.

Name:SINGULAR VALUE DECOMPOSITION TECHNIQUES - SVD

Description:In this second module, we will study a new family of collaborative filtering techniques based on dimensionality reduction and matrix factorization approaches, all inspired by SVD (Singular Value Decomposition). We will see the difference between memory-based and model-based recommender systems, discussing their limitations and advantages. In particular, we will learn how to turn basic matrix factorization algorithms from memory-based into model-based approaches. We will also analyse a new important parameter, the number of latent features. We will learn how to choose the correct number of latent features in order to provide personalised recommendations and to reduce the risk of overfitting historical data.

Name:HYBRID AND CONTEXT AWARE RECOMMENDER SYSTEMS

Description:In this third module, we will see how to combine two or more basic algorithms, such as collaborative filtering and content-based techniques, into a hybrid recommender system, in order improve the quality recommendations. We will study different hybridization approaches, from the simplest heuristic-based, to the more sophisticated machine learning-based. Thanks to hybrid techniques, we will be able to enrich the input of a collaborative recommender system with either content or contextual information.

Name:FACTORIZATION MACHINES

Description:In this fourth and last module, we will introduce a new advanced technique of collaborative filtering with side information, which is called Factorization Machine (FM), and we’ll see how the input data should be represented when using this technique. With only one mathematical model, based on how you build the input table, we will be able to create a simple matrix factorization algorithm or a sophisticated collaborative filtering algorithm with side information (context, attributes on items or attributes on users). We will also discuss benefits and critical issues of algorithms based on FMs. At the end of the module you will know how to use FMs to mix together different kinds of filtering techniques and how to balance different kinds of input information, playing with coefficients and weights, in order to make better and more sophisticated predictions.

Name:Recsys Challenge (Honors)

Description:The RecSys Challenge is the best way to train your competences: it's a practical exercise which provides a "hands-on" opportunity to put to good use and improve what you've been learning during this course (learning by doing). The application domain is an online store, the dataset we provide contains 4 months of transactions collected from an online supermarket. The main goal of the competition is to discover which item a user will interact with. The RecSys Challenge is optional and it is not required to pass the course. If you complete it, you will receive an Honors designation on your Course certificate.

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