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1. 发明授权

US07328216B2 System and method for personalized search, information filtering, and for generating recommendations utilizing statistical latent class models 有权
标题翻译：用于个性化搜索，信息过滤以及使用统计潜在类模型生成建议的系统和方法
公开(公告)号：US07328216B2
公开(公告)日：2008-02-05
申请号：US10639024
申请日：2003-08-11
申请人： Thomas Hofmann , Jan Christian Puzicha
发明人： Thomas Hofmann , Jan Christian Puzicha
IPC分类号： G06F17/30
CPC分类号： G06F17/30699 , Y10S707/99934 , Y10S707/99936 , Y10S707/99942
摘要： The system implements a novel method for personalized filtering of information and automated generation of user-specific recommendations. The system uses a statistical latent class model, also known as Probabilistic Latent Semantic Analysis, to integrate data including textual and other content descriptions of items to be searched, user profiles, demographic information, query logs of previous searches, and explicit user ratings of items. The system learns one or more statistical models based on available data. The learning may be reiterated once additional data is available. The statistical model, once learned, is utilized in various ways: to make predictions about item relevance and user preferences on un-rated items, to generate recommendation lists of items, to generate personalized search result lists, to disambiguate a users query, to refine a search, to compute similarities between items or users, and for data mining purposes such as identifying user communities.
摘要翻译：该系统实现了一种用于个性化过滤信息和自动生成用户特定建议的新方法。系统使用统计潜在类别模型（也称为概率潜在语义分析）来整合包括要搜索的项目的文本和其他内容描述的数据，用户简档，人口统计信息，先前搜索的查询日志以及项目的显式用户评级。系统基于可用数据学习一个或多个统计模型。一旦有其他数据可以重新学习。已经学习的统计模型以各种方式被利用：对未评级项目进行项目相关性和用户偏好的预测，以产生项目的推荐列表，生成个性化搜索结果列表，消除用户查询的歧义，以改进搜索，计算项目或用户之间的相似性，以及数据挖掘目的，如识别用户社区。

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