基于动态和静态偏好的兴趣点推荐算法

针对大多数现有主流兴趣点(POI)推荐算法忽略了融合用户复杂动态偏好和一般静态偏好建模的复杂性问题,提出一个融合复杂动态用户偏好和一般静态用户偏好的POI推荐算法CLSR.首先,在复杂动态偏好建模过程中,基于用户的签到行为及其中的跳过行为设计一个混合神经网络,实现用户的复杂动态兴趣的建模;其次,在一般静态偏好建模过程中...

Point-of-interest recommendation algorithm combing dynamic and static preferences

Since most existing Point-Of-Interest (POI) recommendation algorithms ignore the complexity of the modeling of the fusion of user dynamic and static preferences, a POI recommendation algorithm called CLSR (Combing Long Short Recommendation) was proposed that combined complex dynamic user preferences and general static user preferences. Firstly, in the process of modeling complex dynamic preferences, a hybrid neural network was designed based on the user's check-in behaviors and the skip behaviors in check-in behaviors to achieve the modeling of complex dynamic interests of the user. Secondly, in the process of general static preference modeling, a high-level attention network was used to learn the complex interactions between the user and POIs. Thirdly, a multi-layer neural network was used to further learn and express the above dynamic preferences and static preferences. Finally, a unified POI recommendation framework was used to integrate the preferences. Experimental results on real datasets show that, compared with FPMC-LR (Factorizing Personalized Markov Chain and Localized Region), PRME (Personalized Ranking Metric Embedding), Rank-GeoFM (Ranking based Geographical Factorization Method) and TMCA (Temporal and Multi-level Context Attention), CLSR has the performance greatly improved, and compared to the optimal TMCA among the comparison methods, the proposed algorithm has the precision, recall and normalized Discounted Cumulative Gain (nDCG) increased by 5.8%, 5.1%, and 7.2% on Foursquare dataset, and 7.3%, 10.2%, and 6.3% on Gowalla dataset. It can be seen that CLSR algorithm can effectively improve the results of POI recommendation.