阅读说明：本技术 一种视频冷启动推荐方法及系统 (Video cold start recommendation method and system ) 是由 李文杰 范俊 张智伟 顾湘余 于 2019-11-08 设计创作，主要内容包括：本发明公开了一种视频冷启动推荐方法及系统,其中,推荐方法包括：S1、基于Inception网络、降维处理为新视频生成视频向量；S2、将所述视频向量存储在Faiss中；S3、采用用户最近观看的5个视频将其对应视频向量求和取均值作为用户向量,对Faiss进行索引；S4、为用户返回与用户向量距离小的视频向量所对应的视频。本发明将新视频截帧处理成多个图片,通过为每幅图片生成特征向量以生成视频向量,基于Faiss进行向量索引进行视频推荐,实现了视频的冷启动推荐,复杂度低,推荐效率高。(The invention discloses a video cold start recommendation method and a video cold start recommendation system, wherein the recommendation method comprises the following steps: s1, generating a video vector for the new video based on the inclusion network and the dimensionality reduction processing; s2, storing the video vector in Faiss; s3, adopting 5 videos recently watched by a user to sum and take the average value of the corresponding video vectors as a user vector, and indexing Faiss; and S4, returning the video corresponding to the video vector with small distance from the user vector to the user. According to the method, the new video frame is cut into a plurality of pictures, the feature vector is generated for each picture to generate a video vector, and the video recommendation is performed based on Faiss vector indexing, so that the cold start recommendation of the video is realized, the complexity is low, and the recommendation efficiency is high.)

1. A video cold start recommendation method is characterized by comprising the following steps:

s1, generating a video vector for the new video based on the inclusion network and the dimensionality reduction processing;

s2, storing the video vector in Faiss;

s3, adopting 5 videos recently watched by the user to sum and take the average value of the corresponding video vectors as the user vector to index the Faiss;

and S4, returning the video corresponding to the video vector with small distance from the user vector to the user.

2. The video cold start recommendation method according to claim 1, wherein the step S1 specifically comprises:

s1.1, performing frame cutting processing on the video, and decomposing the video into a plurality of pictures;

s1.2, sequentially inputting the pictures into an increment network, and generating a D-dimensional feature vector for each frame of picture;

s1.3, combining the feature vectors corresponding to each frame of picture to generate an N x D2-dimensional matrix, wherein N is the frame number of video extraction;

s1.4, reducing the dimension of the 2-dimensional matrix of N x D to generate a 2-dimensional matrix of (2+ K) x D, wherein K < N;

and S1.5, performing principal component analysis and whitening dimensionality reduction on the 2-dimensional matrix of the (2+ K) × D.

3. The video cold start recommendation method according to claim 2, wherein the step S1.4 is specifically:

extracting 1-order information, 2-order information and sequence number statistical information from the 2-dimensional matrix of N x D, wherein the 1-order information refers to the average value of the 2-dimensional matrix of N x D on columns; the 2 nd order information refers to the variance of a 2-dimensional matrix of N x D on columns; the sequence number statistical information refers to the first K big value of the 2-dimensional matrix of N x D on the column; the 1 st order information and the 2 nd order information are D-dimensional vectors, and the sequence number statistical information is a 2-dimensional matrix of K x D; and splicing the 1 st order information, the 2 nd order information and the sequence number statistical information to form a 2-dimensional matrix of (2+ K) x D.

4. The video cold start recommendation method according to claim 2, wherein said inclusion network is a well-trained model of google open source.

5. The video cold start recommendation method according to claim 2, wherein said step S4 comprises:

and calculating the distance between the user vector and the video vector stored in the Faiss, sorting the vectors from small to large according to the distance between the vectors, and returning the video corresponding to one or more vectors in the front of the sorting.

6. A video cold start recommendation system, comprising:

the video vector generation module is used for generating a video vector for the new video based on the inclusion network and the dimension reduction processing;

a storage module for storing the video vector in Faiss;

the index module is used for adopting 5 videos recently watched by a user to sum and take the average value of the corresponding video vectors as the user vector to index the Faiss;

and the recommending module is used for returning the video corresponding to the video vector with small distance from the user vector to the user.

7. The video cold start recommendation system according to claim 6, wherein said video vector generation module comprises:

the decomposition module is used for carrying out frame cutting processing on the video and decomposing the video into a plurality of pictures;

the image feature vector generation module is used for sequentially inputting the plurality of images into an increment network and generating D-dimensional feature vectors for each frame of image;

the initial video vector generation module is used for combining the feature vectors corresponding to each frame of picture to generate an N x D2-dimensional matrix, wherein N is the frame number of video extraction;

a first dimension reduction module that reduces the dimension of the 2-dimensional matrix of N x D to generate a 2-dimensional matrix of (2+ K) x D, wherein K < N;

and the second dimension reduction module is used for performing principal component analysis and whitening dimension reduction on the 2-dimensional matrix of (2+ K) × D.

8. The video cold start recommendation system according to claim 7, wherein said first dimension reduction module comprises:

extracting 1-order information, 2-order information and sequence number statistical information from the 2-dimensional matrix of N x D, wherein the 1-order information refers to the average value of the 2-dimensional matrix of N x D on columns; the 2 nd order information refers to the variance of a 2-dimensional matrix of N x D on columns; the sequence number statistical information refers to the first K big value of the 2-dimensional matrix of N x D on the column; the 1 st order information and the 2 nd order information are D-dimensional vectors, and the sequence number statistical information is a 2-dimensional matrix of K x D; and splicing the 1 st order information, the 2 nd order information and the sequence number statistical information to form a 2-dimensional matrix of (2+ K) x D.

9. The video cold start recommendation system according to claim 7, wherein said inclusion network is a well-trained model of google open source.

10. The video cold start recommendation system according to claim 7, wherein said recommendation module comprises:

and calculating the distance between the user vector and the video vector stored in the Faiss, sorting the vectors from small to large according to the distance between the vectors, and returning the video corresponding to one or more vectors in the front of the sorting.