基于用户长短期历史的多兴趣召回算法OA北大核心CSTPCD

With the rapid development of the internet era,users are facing the problem of information overload,and recommendation systems have emerged.Recommendation systems are generally divided into two stages:the recommendation recall stage and the recommendation ranking stage,with the main purpose of the recommendation recall stage being to select a part of the candidate set to reduce the computing load in the recommendation ranking stage.A multi-interest personalized recommendation system learns various users'interest preferences for each user.However,current multi-interest recall algorithms only consider users'short-term history and ignore the rich information contained in users'long-term history.To address this issue,this paper proposes a multi-interest recall algorithm based on users'long and short-term history.The algorithm models users'long and short-term interest preferences through different neural network model structures and uses a gate fusion network to fuse users'long and short-term interest preferences to ultimately obtain users'multiple interest preferences,achieving personalized recommendation recall.The effectiveness of the model is demonstrated through experiments on two public datasets.