Dynamic Group Discovery Based on Density Peaks Clustering

Group recommendation has recently received wide attention due to its significance in real applications. As a premier step of group recommendation, group discovery is very important and discovery results will impact a lot on the performance of group recommendation. The higher similarity the groups have, the better effectiveness and stability the recommendation results will possess. However, current group discovery methods seldom consider the dynamicity of users’ tendency with variance of time context, nor do they support the existence of groups overlapping. In order to address the problems above, a dynamic group discovery method based on density peaks clustering (DGD-BDPC) is put forward in this paper. In the proposed DGD-BDPC method, quantitative users’ dynamic tendency is firstly obtained by dynamic poisson factorization. And secondly, users’ tendency under different time nodes for various items will be predicted with the employment of high order singular value decomposition (HOSVD) and user sets with high similarity will then be built according to users’ tendency. Finally, user sets will be clustered with a modification of density peaks clustering algorithm and group discovery will be realized successfully. Experimental results show that the proposed dynamic group discovery method based on density peaks clustering has higher accuracy, lower error and better stability compared with some other methods.