Abstract: A user query for items is received in a first language and translated from the first language to a second language. A result set in the second language that meets the query is obtained and is translated into the first language for presentation to the user. User feedback is used to build an ontology for optimizing the translation from the first language to the second language based on query context and the feedback. Query context may include information determined by learning semantic relationships between keywords in the query. Optimizing may include building an ontology used by a machine translator to translate key words from the first language to the second language. The number of items in the result set are measured or information is abstracted from the feedback and correlated to ontological information of the result set. The system adapts to changes in meanings in the first language over time.

Abstract: A user query for items is received in a first language and translated from the first language to a second language. A result set in the second language that meets the query is obtained and is translated into the first language for presentation to the user. User feedback is used to build an ontology for optimizing the translation from the first language to the second language based on query context and the feedback. Query context may include information determined by learning semantic relationships between keywords in the query. Optimizing may include building an ontology used by a machine translator to translate key words from the first language to the second language. The number of items in the result set are measured or information is abstracted from the feedback and correlated to ontological information of the result set. The system adapts to changes in meanings in the first language over time.

Abstract: A system receives original content from a user for translating to translated content. If a machine is to be used for translating, the system determines whether the machine-translated content is to be used as the translated content, or whether the machine-translated content should be transmitted to human translators for scoring or review. If the machine-translated content is not to be used as the translated content, it is sent to human translators for scoring or review. If the machine-translated content is to be used as the translated content, the machine-translated content may still be transmitted to human translators for scoring or review, the results used for machine learning. If a machine is not to be used for translating, the original content is sent to human translators for translating. The foregoing determinations are made based on user information or on statistical analysis.

Abstract: A user query for items is received in a first language and translated from the first language to a second language. A result set in the second language that meets the query is obtained and is translated into the first language for presentation to the user. User feedback is used to build an ontology for optimizing the translation from the first language to the second language based on query context and the feedback. Query context may include information determined by learning semantic relationships between keywords in the query. Optimizing may include building an ontology used by a machine translator to translate key words from the first language to the second language. The number of items in the result set are measured or information is abstracted from the feedback and correlated to ontological information of the result set. The system adapts to changes in meanings in the first language over time.

Abstract: A system receives original content from a user for translating to translated content. If a machine is to be used for translating, the system determines whether the machine-translated content is to be used as the translated content, or whether the machine-translated content should be transmitted to human translators for scoring or review. If the machine-translated content is not to be used as the translated content, it is sent to human translators for scoring or review. If the machine-translated content is to be used as the translated content, the machine-translated content may still be transmitted to human translators for scoring or review, the results used for machine learning. If a machine is not to be used for translating, the original content is sent to human translators for translating. The foregoing determinations are made based on user information or on statistical analysis.

Abstract: A user query for items is received in a first language and translated from the first language to a second language. A result set in the second language that meets the query is obtained and is translated into the first language for presentation to the user. User feedback is used to build an ontology for optimizing the translation from the first language to the second language based on query context and the feedback. Query context may include information determined by learning semantic relationships between keywords in the query. Optimizing may include building an ontology used by a machine translator to translate key words from the first language to the second language. The number of items in the result set are measured or information is abstracted from the feedback and correlated to ontological information of the result set. The system adapts to changes in meanings in the first language over time.

Abstract: Techniques for controlling visibility settings of data entities (for example but not limited to item listings, items, products and services, stock keeping units (SKUs) reviews, collections, lists, adverts, etc.) on a networked commerce system are described. According to various embodiments, a seller specification of an item is received, via a user interface, in connection with a request to post item information on a networked commerce system. Entity visibility compliance rules regarding the online sale of items in one or more regions are then accessed. Thereafter, the item specified by the seller is determined to be subject to a particular entity visibility compliance rule in a particular region. An entity visibility setting may then be adjusted for that item information throughout the networked commerce system in that particular region, based on the particular entity visibility compliance rule.

Abstract: Providing country-specific recommendations for selling an item including detecting the listing of an item that is being listed for sale by a seller in a first country. Responsive to the detecting, determining information to be added to the listing in respect of a possible sale of the item in a second country, and informing the seller of the determined information. The determined information may be obtained using a data analytics method that works on one or more historical data logs to mine information relating to factors that may aid or improve sale of the item in the second country. The determined information may also be determined using a data analytics method that works on one or more historical data logs to mine information that indicates that it is attractive to list the item for sale in the second country.

Abstract: In various embodiments, a system and method for providing cross-border transaction buying assistance are provided. In example embodiments, an option is presented to a cross-border buyer to purchase an item from a domestic networked commerce system using a buying agent. Based on a purchase of the item using the buying agent, instructions are sent to a seller of the item to ship the item to a domestic location corresponding to the buying agent. Once received by the buying agent, the buying agent is responsible for managing delivery of the item to the cross-border buyer from the domestic location.