Product Recommendation Algorithm Combining Network Structure and Text Attributes

Most traditional recommendation algorithms rely on common scoring items between users when making product recommendations. The data is highly sparse and the recommendation effect is not good. Therefore, an improved collaborative filtering recom-mendation algorithm is proposed. Based on user purchase rec-ords, the algorithm uses a representation learning method to construct a user product network, obtains the low-dimensional em-bedded semantic relationship between users and product no-des, and uses cosine similarity to measure the semantic similarity between products. Then, according to the hidden Dirichlet topic distribution model, the topic features of the products are obtained, and the cosine similarity is used to calculate the similarity of the topic features between the products. The linear fusion method is adopted to effectively alleviate the data sparse problem and improve the recommendation performance. Through Amazon product reviews Data set to verify the effectiveness of the recommendation algorithm.