Abstract：Recommendation system is essentially an information retrieval tool,which explores useful information and present it to specific individuals.By aggregating group preferences through different aggregation strategies,Group recommendation system help group users to access current hot interest points.The traditional group recommendation model does not consider the impact of time factor on user interest point selection,while the recommendation algorithm based on collaborative filtering is often sensitive to data sparsity.This paper proposes a hybrid recommendation model(AGRT),which integrates K-means clustering algorithm and implicit semantic model(LFM)technology,and applies it to group interest points.taking into account the user in different time points of different preferences,The AGRT model uses K-means algorithm to cluster user data sets based on time points,divide them into different clusters,recommend interest points on user data clusters which are closest to the current recommendation time,decompose user data using LFM implicit semantics model,and obtain user′s score data on Unrated sites by multiplying the decomposition matrix again,so as to solve the problem of sparsity of user data.The experimental results show that the AGRT model improves 5.19% and 2.06% compared with HAaB under the condition of low similarity(random)group and high similarity group.
陶永才,曹朝阳,石磊,卫琳. 一种结合时间因子聚类的群组兴趣点推荐模型[J]. 小型微型计算机系统, 2020, 41(2): 356-360.
TAO Yong-cai,CAO Zhao-yang,SHI Lei,WEI Lin. Group POI Recommendation Model Based on Time Factor Clustering. Journal of Chinese Computer Systems, 2020, 41(2): 356-360.