The invention discloses a location
social network service recommendation method based on trajectory
collaborative filtering and Mama. The method comprises the following steps: firstly, collecting service interaction data of a user in a location
social network; carrying out representation modeling on the service interaction behavior based on static and dynamic joint representation learning, and generating a point-of-interest static representation embedding vector and a user interest
signal embedding vector; constructing a service interaction behavior
sequence modeling network based on a
state space machine model Mamba, embedding and inputting a user interest
signal into the network, modeling a user long and short term interest state transition mode field through the
state space model, and outputting a prediction interest point matching embedding vector; and finally, optimizing the model through a minimized
cross entropy loss function, generating a user interaction service recommendation
list based on the normalized
score matrix, and taking the first K interest points with the highest
score as recommendation results. According to the method, a unified user long-term interest
transfer mode field and a short-term interest response mode are constructed, and the problem of popularity bias of a recommendation
system is relieved.