Fast Online SVD Revisions for Lightweight Recommender Systems
Fast Online SVD Revisions for Lightweight Recommender Systems
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DOI：
10.1137/1.9781611972733.4
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发表时间：
2003
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影响因子：
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通讯作者：
Matthew Brand
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文献类型：
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作者：
Matthew Brand
奇异值分解（SVD）是许多数据建模/挖掘算法的基础，但SVD算法通常具有二次复杂度，并且需要随机访问完整的数据集。这在大多数数据挖掘设置中是有问题的。我们详细介绍了一个家庭的顺序更新规则添加数据的“瘦”SVD数据模型，修改或删除数据已经纳入模型，并调整模型时，数据生成过程表现出非平稳性。我们还利用SVD来估计不完整数据的最可能完成。我们使用这些方法来建模描述消费者/产品评级表的数据流，其中行和列的片段以随机顺序到达，并且各个表条目在任何时候被任意添加，修改或撤回。这些纯在线规则具有非常低的时间复杂度，并且需要不大于单个用户评级的数据流缓存。我们展示了这个计划在一个交互式的图形电影推荐，预测和显示评级/排名的数千个电影的标题在实时用户调整评级的一个小的任意一组探测电影。系统通过响应于用户评级来修改SVD来在使用时“学习”。用户可以异步加入、添加评级、添加电影、修改评级、获得推荐以及从模型中删除自己。
The singular value decomposition (SVD) is fundamental to many data modeling/mining algorithms, but SVD algorithms typically have quadratic complexity and require random access to complete data sets. This is problematic in most data mining settings. We detail a family of sequential update rules for adding data to a “thin” SVD data model, revising or removing data already incorporated into the model, and adjusting the model when the data-generating process exhibits nonstationarity. We also leverage the SVD to estimate the most probable completion of incomplete data. We use these methods to model data streams describing tables of consumer/product ratings, where fragments of rows and columns arrive in random order and individual table entries are arbitrarily added, revised, or retracted at any time. These purely online rules have very low time complexity and require a data stream cache no larger than a single user’s ratings. We demonstrate this scheme in an interactive graphical movie recommender that predicts and displays ratings/rankings of thousands of movie titles in real-time as a user adjusts ratings of a small arbitrary set of probe movies. The system “learns” as it is used by revising the SVD in response to user ratings. Users can asynchronously join, add ratings, add movies, revise ratings, get recommendations, and delete themselves from the model.