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    • 4. 发明公开
    • RECOMMENDATION SYSTEM AND METHOD FOR A MOBILE DEVICE BASED ON RAW DATA WHICH IS COLLECTED FROM SENSORS OF THE MOBILE DEVICE
    • 用于在移动设备基传感器从所述移动设备建议的系统和方法收集的原始数据
    • EP3091497A1
    • 2016-11-09
    • EP16166381.0
    • 2016-04-21
    • Deutsche Telekom AG
    • Shapira, BrachaRokach, LiorBar, ArielUnger, MosheChizi, Barak
    • G06Q30/06G06Q30/02
    • G06Q30/0631G06Q30/0267
    • The present invention refers to a recommendation system which comprises:
      e. extracting unit at a user mobile device for extracting latent raw sensors data from one or more sensors of the mobile device;
      f. communication means for conveying said latent raw sensors data, optionally together with passive data, to a query formation module at a remote server;
      g. said query formation module at the remote server for forming a query from said latent raw sensors data, and optionally also from said passive data and from a user profile data, if exists within said query formation module, and conveying the query into a recommender at said remote server;
      h. said recommender at the remote location for: (i) receiving said query from said query formation module; (ii) processing said query against a general model and a recommendation library, to obtain a product recommendation R; and (iii) conveying said product recommendation R to the user mobile device.
    • 本发明涉及一种推荐系统,该系统包括:e。 在用于提取从所述移动设备的一个或多个传感器潜原始传感器数据的用户移动装置提取单元; F。 通信装置,用于传送所述潜原始传感器数据,具有被动数据任选地,在一个远程服务器的查询形成模块; 克 所述用于从所述被动数据和从用户简档数据从所述形成的查询潜原始传感器数据,因此可选地,如果所述查询形成模块内时,和传送查询分成在所述一个推荐的远程服务器查询形成模块 REMOTESERVER; 小时。 所述推荐器在远程位置:(i)从查询接收所述查询,所述形成模块; (ii)加工所述查询针对一般模型和推荐文库,以获得产物推荐R等 和(iii)传送所述产品推荐R键的用户移动装置。
    • 5. 发明公开
    • METHOD AND SYSTEM FOR RECOMMENDING AN ITEM TO A USER
    • VERFAHREN UND SYSTEM ZUM EMPFEHLEN EINES GEGENSTANDES AN EINEN BENUTZER
    • EP2960849A1
    • 2015-12-30
    • EP15174025.5
    • 2015-06-26
    • Deutsche Telekom AG
    • Grolman, EditaBar, ArielShapira, BrachaRokach, LiorDayan, Aviram
    • G06Q30/06G06Q30/02
    • G06Q30/0631G06Q30/0282
    • A computer implemented method for recommending an item to a user, according to which a plurality of users each of which with a known profile, interface with a corresponding computerized device and binary event-specific, user-item source matrices are generated, for indicating whether a given user performed a given explanatory event included in a corresponding source matrix. All users included in the source matrices are grouped to a profile-specific cluster and items included in the source matrices are grouped to an item category cluster to generate a predictive event book matrix which indicates the probability that an event unknown to have been performed by the given user will be performed for each profile-specific and item category cluster combination included in the one or more source matrices. Each user gets recommendation about an item included in the source matrices, which has a highest probability.
    • 一种用于向用户推荐项目的计算机实现的方法,根据该方法,生成具有已知简档的多个用户与对应的计算机化设备和二进制事件特定的用户项目源矩阵的接口,用于指示是否 给定用户执行包括在相应源矩阵中的给定说明事件。 包括在源矩阵中的所有用户被分组到特定于配置文件的集群,并且包括在源矩阵中的项目被分组到项目类别集群以生成预测事件簿矩阵,其指示事件未被执行的事件的概率 给定用户将针对包含在一个或多个源矩阵中的每个特定于个人资料和项目类别集群组合执行。 每个用户获得关于源矩阵中包括的具有最高概率的项目的推荐。
    • 10. 发明公开
    • SELF TRANSFER LEARNING RECOMMENDATION METHOD AND SYSTEM
    • EMPFEHLUNGSVERFAHREN UND -SYSTEMFÜRSELBSTÜBERTRAGENDESLERNEN
    • EP2983123A1
    • 2016-02-10
    • EP15179606.7
    • 2015-08-04
    • Deutsche Telekom AG
    • Grolman, EditaIny, YoniBar, ArielShapira, BrachaRokach, LiorDayan, Aviram
    • G06Q30/02G06Q30/00
    • G06Q30/00G06Q30/02
    • A computer implemented method for recommending an item to a user in big data systems, according to which a binary matrix of ratings of items is generated by users for a full target domain and sub-matrixes with high correlation between the users, items and their ratings are discovered in the binary matrix. The density of the sub-matrixes in the binary matrix is calculated and a source domain matrix is populated according to the results, based on the density calculated in the discovered sub-matrixes. Then top-N matrixes with highest densities are selected. Codebooks, which contains different user-item rating patterns, are constructed for each of the selected sub-matrixes, which are sub-domains and constructing a codebook for the matrix with the full rating dataset. Finally the captured different user-item rating patterns are projected to the full domain dataset, based on the constructed codebooks.
    • 一种计算机实现的方法,用于向大数据系统中的用户推荐项目,根据该方法,用户为完整目标域生成项目评级的二进制矩阵,并且在用户,项目及其评级之间具有高相关性的子矩阵 在二进制矩阵中被发现。 计算二进制矩阵中子矩阵的密度,并根据结果,根据发现的子矩阵中计算的密度填充源域矩阵。 然后选择具有最高密度的top-N矩阵。 为每个选定的子矩阵构建包含不同用户项目评估模式的代码簿,这些子矩阵是子域,并使用完整的分级数据集构建矩阵的码本。 最后,基于构建的码本,将捕获的不同用户项目评级模式投影到完整域数据集。