A multimedia data processing method and device

Abstract

Embodiments of the invention disclose a multimedia data processing method and device. The method comprises the following steps of generating a multimedia data operation behavior matrix according to operation behaviors of a history user group for more data in a preset multimedia database; computing concealed feature vectors respectively corresponding to the multimedia data and user feature vectors respectively corresponding to history users based on a sparse self-encoding neural network according to the multimedia data operation behavior matrix; and when a recommendation request corresponding to a target user is received and the history user group includes the target user, obtaining a plurality of multimedia data in personal operation behavior information of the target user and carrying out recommendation processing on the multimedia data in the personal operation behavior information according to the user feature vector corresponding to the target user and the concealed feature vectors respectively corresponding to the multimedia data in the personal operation behavior information. Through adoption of the method and the device, the recommended song can be guaranteed to be liked by the user, so that the recommendation effect is improved.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a multimedia data processing method and device. Background technique [0002] With the development of Internet technology, various applications emerge in an endless stream, for example, instant messaging applications, game applications, multimedia data applications, and so on. Taking the multimedia data application as an example, the user can listen to various songs through the multimedia data application, and can also recommend corresponding songs to the user by inferring the user's favorite songs. At present, the way of guessing the songs liked by the user may include: identifying the songs favorited by the user (or downloaded) as the songs liked by the user, therefore, it can be speculated that the songs liked by the user include Similar songs, and then recommend these similar songs to users. When the user does not collect (or download) the song, the song that has been ...

AI Technical Summary

Problems solved by technology

However, the completely played song does not represent the song that the user is listening to (for example, the user temporarily leaves the computer, while the music player in the computer continues to play), and it cannot represent the song that the user likes, so if the complete playback is performed directly If the completed song is identified as the user's favorite song, there is no guarantee that the recommended song is the user's favorite song, resulting in poor recommendation effect.

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