融合用户兴趣漂移的Top-N推荐算法

Top-N recommendation algorithm based on user′s interest drift

传统推荐算法多基于用户兴趣的静态属性获得用户偏好,忽略了用户兴趣漂移问题,为此,提出了解决该问题的融合用户兴趣漂移的Top-N推荐算法。利用长短期记忆网络(LSTM,long short term memory)处理时序数据的优势表示用户短期兴趣漂移规律,用矩阵分解得到的固定向量表示用户的长期兴趣,将注意力机制纳入LSTM隐藏状态的表示中来获取用户长短期兴趣关联。实验结果表明,所提算法与当前流行算法相比,在Top-N项目推荐中具有更优性能。

Traditional recommendation algorithms are mostly based on the static attributes of users without considering the drift of user′s interest.To this end,Top-N recommendation algorithm integrating user′s interest drift detection is proposed.The new algorithm makes full use of long short-term memory(LSTM)in processing time series data to represent short-term interest shift of users,compromises a fixed vector obtained by matrix factorization to represent long-term interest of users,and incorporates the attention mechanism into the representation of hidden state of the long short-term memory network to obtain the effect of the user′s long-term interest on the short-term interest.Compared with current popular algorithms,performance of the proposed algorithm is superior in Top-N item recommendation.