动态时间序列是许多现代推荐系统的关键特征,主要是为了寻求基于用户最近执行的动作来捕获用户活动的"上下文",然而大部分基于长短期记忆网络的序列模型只考虑了用户的短期兴趣,忽略了长期兴趣.为提升序列推荐的性能,提出一种基于LSTM的长短期偏好序列推荐方法LLSPRec(Long Short-term Preference Recommen-dation Based on LSTM).该方法使用LSTM对用户的时间序列进行建模,聚合了序列之间的相关特征信息,得到用户的近期偏好,通过距离度量学习对用户本身和候选项目距离进行建模,得到用户的长期偏好,并根据用户的意图动态地整合用户的长期和近期偏好,从而准确地描述用户兴趣,提高推荐结果的多样性.
Research on long short-term preference sequence recommendation algorithm based on LSTM
Dynamic time series is a key feature of many modern recommendation systems.Its primary aim is to capture the"context"of user activities based on their most recent actions.However,most LSTM-based sequence models only consider the user's short-term interests,neglecting their long-term interests.To enhance the performance of sequence recommendations,a Long Short-term Preference Recommendation Based on LSTM(LLSPRec)method is proposed here.This method models the user's time series with LSTM,aggre-gates relevant feature information between sequences to obtain the user's recent preferences,and models the distance between the user and candidate items using distance metric learning to capture the user's long-term preferences,dynamically integrating the user's long-term and short-term preferences according to their intentions,thereby accurately describing user interests and improving the diversity of recommendation results.
LSTMmetric learningdynamic time seriessequence recommendationelement correlation
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