Abstract: A search data processing method includes: obtaining a media search graph including a query node, a media node, and an association node; obtaining first training sample pairs, including a positive node pair and a negative node pair, from the media search graph, sampling the media search graph based on meta-paths respectively corresponding to the query and the media data, to obtain sampling sub-graphs; inputting the sampling sub-graphs to an initial graph neural network to obtain respective initial semantic features and form a semantic feature pair corresponding to the first training sample pair; and training the initial graph neural network based on a difference between semantic feature pairs of the positive node pair and the negative node pair, to obtain a target graph neural network that is configured to determine a target semantic feature corresponding to a query node or a media node.

Abstract: This application discloses a search result reordering method performed by a computer device. The method includes: displaying a search result page corresponding to a search session, the search result page displaying a first content sequence obtained based on a search keyword corresponding to the search session, and the first content sequence including a plurality of media contents; obtaining at least one real-time interactive behavior performed by a target object on the search result page; determining media contents other than a target media content associated with the at least one real-time interactive behavior as to-be-sorted media contents in the first content sequence; and reordering the to-be-sorted media contents to obtain a second content sequence when the at least one real-time interactive behavior satisfies a reordering trigger condition, and updating the search result page based on the second content sequence.