关键词: 用户偏好模型, 动态网页数据, 数据交互式查询, 粒子群优化算法, 空间维度

Abstract: In order to improve the speed, accuracy and efficiency of web data query, we proposed a dynamic web data interactive query algorithm based on user preferences. The user preference model was built to increase the evolutionary individual adaptability of the preference combinations, and the adaptive value was  comprehensively calculated. Secondly, in order to prevent data redundancy and duplication, based on interest similarity, query data and duplicate data with high similarity were separated to identify the properties of network data. Finally, the particle swarm optimization algorithm was used to find the optimal interactive query scheme of dynamic web page data. The experimental results show that the quality of the query result set of the proposed algorithm is above 0.95 under the influence of the dataset cardinality, under the influence of the maximum dimension of the query, the quality of the query result set of the proposed algorithm is above 0.96, indicating  that the proposed algorithm has short query time, high precision of the result set and strong adaptability.

Key words: user preference model, dynamic web page data, interactive data query, particle swarm optimization algorithm, spatial dimension