【摘要】 现有音乐推荐系统在大规模隐式反馈场景下存在推荐困难的问题,提出大规模隐式反馈的词向量音乐推荐模型(Word-Embedding Based Implicit Music Recommender).本模型借鉴了自然语言处理领域的Word2Vec技术,通过学习用户音乐收藏播放记录里的歌曲共现信息,获得用户、音乐在分布式空间的低维、紧致的向量表示,从而得到用户、音乐之间的相似度进行推荐,并且在理论上论述了Word2Vec技术应用在推荐系统上的正确性.该模型在保证准确率和召回率几乎不变的同时,收敛速度快,占用内存小,试验结果表明该模型有效的解决了大规模隐性反馈场景下音乐推荐困难的问题.更多还原

【Abstract】 A large scale word-embedding based implicit music recommender is proposed to address the problems that most of current recommendation systems cannot work in the scenario of large scale implicit feedback recommendation.This model employs the Word2 Vec technique which is popular in Natural Language Processing in recent years. By learning the songs co-occurrences in the users’ history collections, we can get the distributed representation of users and songs with a low-dimension and dense vector. In this way, we can get the similarities of users and songs which could be used for the recommendation and we also analyze the correctness of application of Word2 Vec technique in recommendation. This model can effectively solve the problem mentioned above with the accuracy remaining the same.In addition, this model can converge faster and take less memory than those of traditional methods.更多还原