A Patent recommendation algorithm based on topic classification and semantic similarity

Patent recommendation algorithms, as an important means of information push, are an important means of solving the information overload of today's massive data. However, traditional recommendation algorithms have problems such as the inability to make full use of user information, system cold start, and sparse data matrix, so this paper proposes a patented recommendation algorithm based on topic classification and semantic similarity. By introducing the Bert neural network, this algorithm extracts keywords from patent titles and abstracts, and then transforms them into word vectors. By using them, the algorithm uses the DBSCAN clustering method to construct patent subject area categories. Combining with SimNet, a text similarity framework, it becomes a holistic analysis model. Inputting patent text to be predicted into trained analysis model, then it can do patent recommendation work. Comparing with the traditional recommendation algorithm, the experiment shows that the algorithm proposed in this paper can obtain a better recommendation effect on the patent recommendation.