Abstract: A method for training a fusion ordering model, a search ordering method, an electronic device and a storage medium, and related to the technical field of artificial intelligence such as deep learning and the like are provided. The method includes: inputting prediction scores of a plurality of targets included in a candidate search result into a fusion ordering model, to obtain a fusion ordering result; collecting feedback information of the plurality of targets included in the fusion ordering result; and updating the fusion ordering model by utilizing the feedback information and a combined function, wherein the combined function is a function constructed by utilizing the plurality of targets. The solution of the embodiment is favorable for balancing the requirements of the plurality of targets, and the obtained updated fusion ordering model can provide a better search ordering result.

Abstract: A method, apparatus and device used to search for content, and a computer-readable storage medium, which relate to the field of data processing. In the method, in response to receiving a search request for a target search term, obtain multiple historical search records related to multiple historical search requests, each historical search record comprising the historical search term targeted by the corresponding historical search request. Then, determine a first historical search record matching the target search term from the multiple historical search records. Furthermore, based on the relationship between multiple historical search records, determine from the multiple historical search records a second historical search record associated with the first historical search record. The method further comprises determining an extended result for the target search term on the basis of the search result corresponding to the second historical search record.

Abstract: A method for training a fusion ordering model, a search ordering method, an electronic device and a storage medium, and related to the technical field of artificial intelligence such as deep learning and the like are provided. The method includes: inputting prediction scores of a plurality of targets included in a candidate search result into a fusion ordering model, to obtain a fusion ordering result; collecting feedback information of the plurality of targets included in the fusion ordering result; and updating the fusion ordering model by utilizing the feedback information and a combined function, wherein the combined function is a function constructed by utilizing the plurality of targets. The solution of the embodiment is favorable for balancing the requirements of the plurality of targets, and the obtained updated fusion ordering model can provide a better search ordering result.