关键词: 时序范围, 最具稳定性, 服务集推荐, 个性化服务, 反向Top-k查询, 背包问题

Abstract: Batch service recommendation has a great application prospect in Service Computing area. However, how to adapt to the frequent changes of the large-scale online user demand to dynamically recommend the most stable service collection is still a challenging technical problem. This paper presents a new recommendation algorithm of services, which uses backtracking to choose determine the users which have some service target. The impact of time variation on user online state, to discover the best service set which can satisfy most user demand stably with changing time, through the dynamic competition optimization of services with the users. To verify the rationality and validity of the proposed algorithm, a series of experiments is maken on real data set from WS-DREAM. Experimental results show that the proposed algorithm can effectively help product providers find the most stably service collection with changing time, thus achieving the maximization of interests.

Key words: changing time, most stably demand, service recommendation, personalization service, reverse Top-k query, knapsack problem