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ROBUST LOW-RANK MATRIX FACTORIZATION WITH MISSING DATA BY MINIMIZING L1 LOSS APPLIED TO COLLABORATIVE FILTERING

机译:通过最小化应用于协同过滤的L1损失,使鲁棒的低阶矩阵分解与数据丢失

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摘要

In this age of information overload and plethora of choices, people increasingly rely on automatic recommender systems to tell them what suits their needs. A very effective approach for creating recommender systems is collaborative filtering, which is the task of predicting the preference/rating that a user would assign to an item based on preference data of that user and preference data of other users. One way to conduct collaborative filtering is through dimensionality reduction. The underlying concept of the approach lies in the belief that there are only a few features (reduced dimensions) that influence the user’s choice. In this paper we use low rank matrix factorization for dimensionality reduction. Singular Value Decomposition (SVD), which is minimizing the L2 norm is the most popular technique to perform matrix factorization. However, in most recommendation system data sets, often the users only rate a small amount of items, which creates missing data. As a result SVD fails. In recent years L1 norm has gained much importance and popularity because it is robust to outliers and missing data. In this thesis we use alternate convex optimization to perform L1 norm minimization to solve the matrix factorization problem and apply it to collaborative filtering. We also review some of the major challenges that collaborative filtering faces today and some of the other techniques used. Additionally, this thesis discusses the importance and future of collaborative filtering in medical applications that concerns the database of patient history (prescriptions/symptoms) and how it can be used as a predictive task for the future of the patient.
机译:在这个信息超负荷和选择过多的时代,人们越来越依赖自动推荐系统来告诉他们什么才是适合他们的需求。创建推荐系统的一种非常有效的方法是协作过滤,它是根据用户的偏好数据和其他用户的偏好数据预测用户将分配给该商品的偏好/等级的任务。进行协作过滤的一种方法是降低维数。该方法的基本概念是相信只有少数几个功能(缩小尺寸)会影响用户的选择。在本文中,我们使用低秩矩阵分解进行降维。最小化L2范数的奇异值分解(SVD)是执行矩阵分解的最流行技术。但是,在大多数推荐系统数据集中,用户通常只对少量项目进行评分,这会导致丢失数据。结果,SVD失败。近年来,L1规范已变得非常重要和流行,因为它对异常值和丢失数据具有鲁棒性。在本文中,我们使用交替凸优化来执行L1范数最小化,以解决矩阵分解问题并将其应用于协同过滤。我们还将回顾协作过滤今天面临的一些主要挑战以及其他一些使用的技术。此外,本文讨论了与患者病史(处方/症状)数据库有关的医疗应用中协同过滤的重要性和未来,以及如何将其用作患者未来的预测任务。

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  • 作者

    Huda Shama M;

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  • 年度 2012
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  • 原文格式 PDF
  • 正文语种 en
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