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    • 1. 发明申请
    • Next Generation Improvements in Recommendation Systems
    • 推荐系统的下一代改进
    • US20140172627A1
    • 2014-06-19
    • US14133818
    • 2013-12-19
    • Kenneth L. LevyNeil E. Lofgren
    • Kenneth L. LevyNeil E. Lofgren
    • G06Q30/06
    • G06Q30/0631G06Q30/02
    • This invention deals with the next generation improvements in recommendation systems. Retailers want to grow their business and increase sales. One embodiment displays recommendations for inside sales during calls to prospects via a CRM. Another embodiment improves genomic cross-sell by summing correlations between attributes. A third embodiment improves cross-channel personalization by linking personal information, preferably via a one-way hash, to a unique customer ID. A fourth embodiment enables a common core mobile app for different retailers. A fifth embodiment identifies a shopper before purchase to provide personal recommendations while shopping. A sixth embodiment utilizes a market place with shared customers for customer acquisition. A seventh embodiment utilizes customers' preferences and characteristics and sales data to influence recommendations. The characteristics can be combined into a shopper psychographic persona to generate recommendations. An eight embodiment is a market place for customers to shop, which is used for customer acquisition for participating retailers. A ninth embodiment shows how to improve search results based upon analysis of purchase data, and correlation of clicks on search results and search terms. A tenth embodiment calculates a buy index based upon value of products purchased versus products viewed to segment shoppers to determine discounts and re-marketing. An eleventh embodiment automates the creation of a dynamic website, usually for responsive design.
    • 本发明涉及推荐系统的下一代改进。 零售商希望发展业务并增加销售额。 一个实施例通过CRM显示对潜在客户的内部销售的建议。 另一个实施方式通过对属性之间的相关性求和来改进基因组交叉销售 第三实施例通过将个人信息(优选地通过单向散列)链接到唯一的客户ID来改进跨渠道个性化。 第四个实施例使不同零售商的通用核心移动应用程序成为可能。 第五实施例在购买之前识别购物者在购物时提供个人建议。 第六个实施例利用共享客户的市场来进行客户获取。 第七个实施例利用客户的偏好和特征以及销售数据来影响推荐。 这些特征可以组合成一个购物心理学角色来产生建议。 八个实施例是客户购物的市场,用于参与零售商的客户获取。 第九实施例示出了如何基于购买数据的分析以及搜索结果和搜索项的点击的相关性来改进搜索结果。 第十实施例基于购买的产品的价值计算购买指数,并根据分组购物者查看的产品计算购买指数,以确定折扣和再营销。 第十一个实施例可自动创建动态网站,通常用于响应式设计。
    • 2. 发明申请
    • Further Improvements in Recommendation Systems
    • 推荐系统的进一步改进
    • US20110282821A1
    • 2011-11-17
    • US13107858
    • 2011-05-13
    • Kenneth L. LevyNeil E. Lofgren
    • Kenneth L. LevyNeil E. Lofgren
    • G06N5/02
    • G06Q30/0631
    • This invention deals with improving recommendation systems. The first embodiment combines rules and recommendations to create automated and intelligent business rules for recommendations. The second embodiment improves recommendations by combining the results of driver products and influencer products, where influencer products only influence the recommendations of the driver products. Influencer products can be related to a specific user. The third embodiment improves recommendations for new items by relating them to original items, such that the sales for the original item is used in the new item when calculating recommendations. The new items may replace the original item, or be a similar item and exist alongside the original item.
    • 本发明涉及改进推荐系统。 第一个实施例结合了规则和建议,以创建自动化和智能的业务规则来推荐。 第二个实施例通过结合驾驶员产品和影响者产品的结果来改进建议,其中影响者产品仅影响驾驶员产品的建议。 影响产品可能与特定用户有关。 第三实施例通过将新项目与原始项目相关联来改进对新项目的建议,使得在计算建议时在新项目中使用原始项目的销售。 新项目可以替换原始项目,或者作为类似项目,并且与原始项目一起存在。
    • 5. 发明申请
    • Recommendation Systems
    • 推荐系统
    • US20100268661A1
    • 2010-10-21
    • US12764091
    • 2010-04-20
    • Kenneth L. LevyNeil E. Lofgren
    • Kenneth L. LevyNeil E. Lofgren
    • G06Q99/00G06F17/30
    • G06Q30/0282G06Q30/02
    • This invention deals with recommendation systems. The first embodiment is an off-the-shelf recommendation system is described, where it is easy to integrate with the website database and uses a web service for recommendations, as well as easy to integrate with email. The system receives client ID, item ID and user ID, and returns recommended item IDs. The recommendations include similar items, related items, related users, items likely to be acted upon by a given user (labeled likely items), and users likely to act upon an item (labeled likely users). The recommendations include categorical training, where recommended items are based upon similar categories, where the category types include as product type and brand. The recommendations include similar-to-related training, where similar items are used to find related items. These two intelligent methods work for items with no, few or numerous actions.
    • 本发明涉及推荐系统。 第一个实施例描述了现成的推荐系统,其中易于与网站数据库集成并且使用web服务来推荐,并且易于与电子邮件集成。 系统收到客户端ID,项目ID和用户ID,并返回推荐的项目ID。 建议包括类似项目,相关项目,相关用户,特定用户可能采取行动的项目(标示为可能的项目),以及可能对某个项目(标记为可能的用户)采取行动的用户。 这些建议包括分类培训,推荐的项目基于类似的类别,类别类别包括产品类型和品牌。 建议包括类似相关的培训,类似的项目用于查找相关项目。 这两种智能方法适用于没有,少量或多种动作的项目。
    • 7. 发明申请
    • Next Generation Improvements In Recommendation Systems
    • US20190220916A1
    • 2019-07-18
    • US16365669
    • 2019-03-26
    • Kenneth L. LevyNeil E. Lofgren
    • Kenneth L. LevyNeil E. Lofgren
    • G06Q30/06G06Q30/02
    • G06Q30/0631G06Q30/02
    • This invention deals with the next generation improvements in recommendation systems. Retailers want to grow their business and increase sales. One embodiment displays recommendations for inside sales during calls to prospects via a CRM. Another embodiment improves genomic cross-sell by summing correlations between attributes. A third embodiment improves cross-channel personalization by linking personal information, preferably via a one-way hash, to a unique customer ID. A fourth embodiment enables a common core mobile app for different retailers. A fifth embodiment identifies a shopper before purchase to provide personal recommendations while shopping. A sixth embodiment utilizes a market place with shared customers for customer acquisition. A seventh embodiment utilizes customers' preferences and characteristics and sales data to influence recommendations. The characteristics can be combined into a shopper psychographic persona to generate recommendations. An eight embodiment is a market place for customers to shop, which is used for customer acquisition for participating retailers. A ninth embodiment shows how to improve search results based upon analysis of purchase data, and correlation of clicks on search results and search terms. A tenth embodiment calculates a buy index based upon value of products purchased versus products viewed to segment shoppers to determine discounts and re-marketing. An eleventh embodiment automates the creation of a dynamic website, usually for responsive design.
    • 9. 发明申请
    • Next Generation Improvements in Recommendation Systems
    • US20170372400A9
    • 2017-12-28
    • US14133818
    • 2013-12-19
    • Kenneth L. LevyNeil E. Lofgren
    • Kenneth L. LevyNeil E. Lofgren
    • G06Q30/06G06Q30/02
    • G06Q30/0631G06Q30/02
    • This invention deals with the next generation improvements in recommendation systems. Retailers want to grow their business and increase sales. One embodiment displays recommendations for inside sales during calls to prospects via a CRM. Another embodiment improves genomic cross-sell by summing correlations between attributes. A third embodiment improves cross-channel personalization by linking personal information, preferably via a one-way hash, to a unique customer ID. A fourth embodiment enables a common core mobile app for different retailers. A fifth embodiment identifies a shopper before purchase to provide personal recommendations while shopping. A sixth embodiment utilizes a market place with shared customers for customer acquisition. A seventh embodiment utilizes customers' preferences and characteristics and sales data to influence recommendations. The characteristics can be combined into a shopper psychographic persona to generate recommendations. An eight embodiment is a market place for customers to shop, which is used for customer acquisition for participating retailers. A ninth embodiment shows how to improve search results based upon analysis of purchase data, and correlation of clicks on search results and search terms. A tenth embodiment calculates a buy index based upon value of products purchased versus products viewed to segment shoppers to determine discounts and re-marketing. An eleventh embodiment automates the creation of a dynamic website, usually for responsive design.