The existing point of interest recommendation methods ignore the internal relationship between different context fac-tors,which leads to the failure to make full use of context factors.Therefore,a multi-dimensional point of interest recommenda-tion algorithm integrating spatio-temporal,social and sequential effects was proposed.The user's activity area was described according to the user's time state and activity trajectory,and the user's time preference degree and activity trajectory similarity degree were explored.The Gaussian distribution model was used to evaluate the user's geographical preference degree.The improved Markov chain algorithm was used to predict the user's probability of visiting the next point of interest.Experimental results show that this algorithm is superior to other algorithms.
关键词
社交网络/兴趣点推荐/时空/社交/顺序影响/活动轨迹/多维
Key words
social networks/point of interest recommendation/space and time/socialize/sequential influence/activity trajectory/multiple-dimensions
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