A Multi-Objective Decision Optimization Algorithm for Recommendation System

This paper proposes a continuous range Skyline query algorithm in obstacle space to solve the problem of low query efficiency with traditional multi-objective decision technology caused by the movement of searcher's position and the change of spatial obstacles in the recommendation system. Firstly, the initial data set composed of object information in the spatial data is reduced according to the characteristics of the static skyline points; then, the distance intersection model is constructed according to the characteristics of the position movement of the querier in the obstacle space, and the pruning strategy is proposed by using the distance intersection model and the attributes of the data objects. According to the pruning strategy, the data objects that have no impact on the query results when the position of the querier moves are filtered out, so as to reduce the redundant data and obtain the filtered candidate data set; finally, according to the non-spatial attributes of the data object and the characteristics of the dominant relationship between them, the events affecting the candidate data set are obtained and used to refine the candidate data set, so as to reduce redundant calculation and query the result set at the current time. Theoretical research and experiments show that the proposed algorithm can improve the query efficiency of multi-objective decision technology when the searcher moves and the position of spatial obstacles changes. Compared with the traditional comparison algorithm, the average efficiency of the query algorithm is improved by about 13% when the size of the data set, the number of obstacles and the query range are increased; for the query of multi-dimensional data information, the average efficiency of the proposed query algorithm is improved by about 11%. © 2022 Xi'an Jiaotong University. All rights reserved.