Abstract: This disclosure provides systems, methods and apparatuses for recommending items to users of a recommendation system. In some implementations, the recommendation system determines a plurality of contribution values based on interactions between a plurality of users and a plurality of items. Each of the plurality of contribution values represents a confidence level that a respective user prefers a respective item. The recommendation system further determines a capacity of each of the plurality of items. The capacity of each item represents a maximum number of users to which the item can be recommended. The recommendation system recommends one or more items of the plurality of items to each of the plurality of users based at least in part on the plurality of contribution values and the capacities of the plurality of items.

Abstract: Certain aspects of the present disclosure provide methods, processing systems, and computer-readable mediums for benchmarking based on company vendor data. Transactions identifying vendors are received for a group of companies whose industry is known. For each identified vendor, a vendor embedding is generated in the form of a vector representing a distribution of transactions for the vendor across industries represented by the companies. For each company, a company embedding is generated in the form of a vector representing an aggregation of vendor embeddings from vendors with which the company has had a transaction. The company embeddings are then clustered using an unsupervised clustering method such as k-Means clustering. For a new company, a company embedding is generated and correlated to the appropriate cluster. Based on the cluster correlated to the new company, data is generated from other companies in the cluster that may be used to benchmark the new company.

Abstract: This disclosure provides systems, methods and apparatuses for recommending items to users of a recommendation system. In some implementations, the recommendation system determines a plurality of contribution values based on interactions between a plurality of users and a plurality of items. Each of the plurality of contribution values represents a confidence level that a respective user prefers a respective item. The recommendation system further determines a capacity of each of the plurality of items. The capacity of each item represents a maximum number of users to which the item can be recommended. The recommendation system recommends one or more items of the plurality of items to each of the plurality of users based at least in part on the plurality of contribution values and the capacities of the plurality of items.

Abstract: A method and system provides access control for sensitive data. An access control system defines a plurality of access policies for gaining access to the sensitive data. Each access policy includes a plurality of rules that indicate whether or not a client machine can gain access to an initial access secret under the policy. If a client machine requests access to the sensitive data, the access control system checks to see if the client machine satisfies the rules of the access policy. If the characteristics of the client machine satisfy the rules of the access policy, then the access control system provides a ticket to the client machine and instructs the client machine to write the ticket to a client machine information database. If the client machine writes the ticket to the client machine information database, then the access control system provides an initial access secret to the client machine.