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    • 3. 发明申请
    • SYSTEM AND METHOD FOR PRIORITIZED PRODUCT INDEX SEARCHING
    • US20180150894A1
    • 2018-05-31
    • US15879300
    • 2018-01-24
    • WAL-MART STORES, INC.
    • Varun SrivastavaYiye RuanYan Zheng
    • G06Q30/06G06F17/30
    • G06Q30/0625G06F16/3326G06F16/3338
    • Various embodiments include a system for grouping a set of distinct records in a database system, the database system comprising a first database cluster H and a second database cluster L. In many embodiments, the system can comprise one or more processing modules and one or more non-transitory memory storage modules storing computing instructions configured to run on the one or more processing modules. In some embodiments, the computer instructions can be configured to perform acts of determining, for each distinct record (i) of the set of distinct records, whether the record is a first priority or a second priority; for each distinct record (i) of the set of distinct records which is determined to be the first priority, storing the record in the first database cluster H, wherein the first database cluster H comprises a first computer server; and for each record (i) of the set of distinct records which is determined to be the second priority, storing the record in the second database cluster L. In these embodiments, the second database cluster L can comprise a second computer server different from the first computer server, and the first computer server can have greater processing power than the second computer server. In some embodiments, the first priority can be a higher priority than the second priority. Other embodiments are also disclosed herein.
    • 10. 发明申请
    • SYSTEM AND METHOD FOR CALCULATING SEARCH TERM PROBABILITY
    • 用于计算搜索条件概率的系统和方法
    • US20160092772A1
    • 2016-03-31
    • US14498170
    • 2014-09-26
    • Wal-Mart Stores, Inc.
    • Varun SrivastavaYiye RuanYan Zheng
    • G06N5/04G06N99/00G06N7/00G06F17/30
    • G06N7/005G06F17/30864G06N99/005
    • A system and method for predicting search term popularity is disclosed herein. A database system may comprise a first database cluster H and a second database cluster L. A machine learning algorithm is trained to create a predictive model. Thereafter, for each record in a database system, the predictive model is used to calculate a probability of the record being accessed. If the calculated probability of the record being accessed is greater than a threshold value, then the record in the first database cluster H; otherwise, the record is placed in the second database cluster L. Training the machine learning algorithm comprises inputting a training feature vector associated with the record into the machine learning algorithm, inputting a cost vector into the machine learning algorithm, and iteratively operating the machine learning algorithm on each record in the set of records to create a predictive model. Other embodiments are also disclosed herein.
    • 本文公开了一种用于预测搜索词流行度的系统和方法。 数据库系统可以包括第一数据库簇H和第二数据库簇L.训练机器学习算法以创建预测模型。 此后,对于数据库系统中的每个记录,使用预测模型来计算正被访问的记录的概率。 如果所计算的被访问的概率大于阈值,则在第一数据库簇H中的记录; 训练机器学习算法包括将与记录相关联的训练特征向量输入到机器学习算法中,将成本向量输入到机器学习算法中,并且迭代地操作机器学习 算法对记录中的每个记录创建一个预测模型。 本文还公开了其它实施例。