  With the rapid development of Internet technology and the comprehensive
popularity of Internet applications, online activities have gradually become an
indispensable part of people's daily life. The original recommendation learning
algorithm is mainly based on user-microvideo interaction for learning, modeling
the user-micro-video connection relationship, which is difficult to capture the
more complex relationships between nodes. To address the above problems, we
propose a personalized recommendation model based on graph neural network,
which utilizes the feature that graph neural network can tap deep information
of graph data more effectively, and transforms the input user rating
information and item side information into graph structure, for effective
feature extraction, based on the importance sampling strategy. The
importance-based sampling strategy measures the importance of neighbor nodes to
the central node by calculating the relationship tightness between the neighbor
nodes and the central node, and selects the neighbor nodes for recommendation
tasks based on the importance level, which can be more targeted to select the
sampling neighbors with more influence on the target micro-video nodes. The
pooling aggregation strategy, on the other hand, trains the aggregation weights
by inputting the neighborhood node features into the fully connected layer
before aggregating the neighborhood features, and then introduces the pooling
layer for feature aggregation, and finally aggregates the obtained neighborhood
aggregation features with the target node itself, which directly introduces a
symmetric trainable function to fuse the neighborhood weight training into the
model to better capture the different neighborhood nodes' differential features
in a learnable manner to allow for a more accurate representation of the
current node features.
