Modeling and application of implicit feedback in personalized recommender systems

Traditional recommendation algorithms usually rely on the user's existing data and historical behavioral records to make recommendations, which often leads to low recommendation accuracy and insufficient personalized experience. To solve these problems, this paper proposes an innovative recommendation algorithm model, neural collaborative filtering with multiple attention mechanism (NCF-MAH). The goal of this model is to enhance the effectiveness of the recommender system. The specific implementation includes constructing a negative sample set and applying matrix decomposition techniques to map user and item IDs to a low-dimensional embedding vector space. In addition, the model processes these embedding vectors using a multi-head attention mechanism to transform them into query vectors, key vectors, and value vectors, and further computes the attention scores and the corresponding weighted sums. Finally, the score prediction is accomplished by fusing the output of the multi-head attention mechanism with the results of the multilayer perceptual machine. The experimental results show that the NCF-MAH model exhibits significant advantages over the baseline model in two key evaluation metrics, hit rate and normalized discount cumulative gain (NDCG), on the MOOC platform and other datasets. Specifically, hit rate and NDCG improved by 13% vs. 9.8% and 15.7% vs. 12.8% when Top-k was set to 10 and 20, respectively.