名字嵌入向量方法

Before entering into a recommender system, an entity name must be embedded into a vector. Some popular models, such as word2vec, are based on the principle words which are in the same syntactic position should embedded into similar vectors. However, sequence of entity names has no syntactic structure, which led to the low quality of name vectors. Based on the principle neighbouring names should embedded into similar vectors, this paper proposes a novel algorithm named name2vec. Name2vec has new features: vector length equals 1, relative weight which has solved the low frequency problem, optimization objective function is mean square error rather than cross entropy. The quality of embedding is measured by the similarity of entity names. On there datasets from WEIBO.COM, name2vec has a better performance than word2veec.