Abstract: The present application discloses an oral care cleaning tablet, and relates to the technical field of an oral care product, wherein the oral care cleaning tablet 1. is made of the following raw materials by weight percentage: 8-25% of a film-forming agent, 15-40% of a surfactant, 10-30% of an washing-friction modifier, 1-5% of a cosolvent, 0.5-3% of a water softener, 0.01-1% antibacterial agent, 1-5% of moisturizing agent, 0.1-1% of a sweetener, 0-3% of a breath freshener, 0-1% pH regulator, 0-0.5% of an essence, and balance of water; wherein the surfactant includes any one or two of an alcohol ether glycosides and a C8-C18 alkyl glycosides; and the washing-friction modifier includes a plant starch.

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 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.