Abstract: The present disclosure provides a method, an apparatus, an electronic device, and a storage medium for recommending multimedia resource, and relates to the field of machine learning. The method includes: acquiring features of the multimedia resource based on a convolutional neural network, where the convolutional neural network comprises N convolutional layers, where N is a positive integer; determining user interest information based on an identifier of a recommended user, where the user interest information is corresponding to the feature of each convolutional layer; determining a first feature matrix based on the convolution of convolution kernel and the feature, where the convolution kernel comprises the user interest information; generating user preference data based on the first feature matrix; and recommending the multimedia resource to the recommended user based on the N generated user preference data.

Abstract: The present disclosure relates to a video rating method, a video rating device, equipment and a storage medium, relating to the field of multimedia. An embodiment of the present disclosure provides a method for automatically rating a video based on features of multiple modals of the video and rating embedding modes. By fusing the features of the multiple modals of the video, the rating of the video is converted into rating embedding in a vector space, and a matching degree between a target feature fusing with the multiple modals and each rating embedding is acquired, the rating of the video is predicted according to the matching degree corresponding to each rating embedding, and the video rating efficiency and accuracy can be improved.

Abstract: The present disclosure provides a method, an apparatus, an electronic device, and a storage medium for recommending multimedia resource, and relates to the field of machine learning. The method includes: acquiring features of the multimedia resource based on a convolutional neural network, where the convolutional neural network comprises N convolutional layers, where N is a positive integer; determining user interest information based on an identifier of a recommended user, where the user interest information is corresponding to the feature of each convolutional layer; determining a first feature matrix based on the convolution of convolution kernel and the feature, where the convolution kernel comprises the user interest information; generating user preference data based on the first feature matrix; and recommending the multimedia resource to the recommended user based on the N generated user preference data.

Abstract: The present disclosure relates to a video rating method, a video rating device, equipment and a storage medium, relating to the field of multimedia. An embodiment of the present disclosure provides a method for automatically rating a video based on features of multiple modals of the video and rating embedding modes. By fusing the features of the multiple modals of the video, the rating of the video is converted into rating embedding in a vector space, and a matching degree between a target feature fusing with the multiple modals and each rating embedding is acquired, the rating of the video is predicted according to the matching degree corresponding to each rating embedding, and the video rating efficiency and accuracy can be improved.

Abstract: A method receives ratings for videos from a first user that is using a video delivery service. A first model includes connection networks where each connection network corresponds to a rating. The method inputs each rating into a connection network in an order. Also, parameters for the ratings and ratings other than the rating received from the first user are modeled in a respective connection network. Values for the set of parameters are trained such that the plurality of connection networks predict conditional probabilities that the first user would provide the rating corresponding to the each connection network in the order. The conditional probabilities are based on the first user providing ratings that are previously located in the order. The parameters are then used to generate a list of videos to recommend to the first user using the first model.

Abstract: In one embodiment, a method generates a plurality of sub-relevance tables including a first set of relevance values between media programs. Each table models relevance values for a single feature in a plurality of features. Labeling results are received that include a second set of relevance values between the media programs. The method combines the sub-relevance tables into a single relevance table that includes a third set of relevance values between the media programs for the plurality of features. The combining generates weights for each of the sub-relevance tables based on the second set of relevance values for the labeling results and the first set of relevance values of the sub-relevance tables that are used to generate the third set of relevance values. A recommendation is provided to a user using the third set of relevance values from the single relevance table and a characteristic of the user.

Abstract: A method receives ratings for videos from a first user that is using a video delivery service. A first model includes connection networks where each connection network corresponds to a rating. The method inputs each rating into a connection network in an order. Also, parameters for the ratings and ratings other than the rating received from the first user are modeled in a respective connection network. Values for the set of parameters are trained such that the plurality of connection networks predict conditional probabilities that the first user would provide the rating corresponding to the each connection network in the order. The conditional probabilities are based on the first user providing ratings that are previously located in the order. The parameters are then used to generate a list of videos to recommend to the first user using the first model.

Abstract: In one embodiment, a method generates a plurality of sub-relevance tables including a first set of relevance values between media programs. Each table models relevance values for a single feature in a plurality of features. Labeling results are received that include a second set of relevance values between the media programs. The method combines the sub-relevance tables into a single relevance table that includes a third set of relevance values between the media programs for the plurality of features. The combining generates weights for each of the sub-relevance tables based on the second set of relevance values for the labeling results and the first set of relevance values of the sub-relevance tables that are used to generate the third set of relevance values. A recommendation is provided to a user using the third set of relevance values from the single relevance table and a characteristic of the user.

Abstract: According to a cost-per-action advertising model, advertisers submit ads with cost-per-action bids. Ad auctions are conducted and winning ads are returned with contextually relevant search results. Each time a winning ad is selected by a user, resulting in the user being redirected to a website associated with the advertiser, a selected impression and a price is recorded for the winning ad. Periodically, an advertiser submits a report indicating a number of actions attributed to the ads that have occurred through the advertiser website. The advertiser is then charged a fee for each reported action based on the recorded prices for the winning ads and based on the number of selected impressions recorded for the winning ads.