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Additionally, current researches also fail to take capacities and categories of the facilities into consideration. To overcome the drawbacks, we introduce a novel model of Multi-characteristic Information based Top-k Location Prediction (MITLP), it captures the spatio-temporal behaviors of customers based on historical trajectories, exploits the social relevancy from their friend relationships, as well as examines the category competitiveness of specific facilities thoroughly. Subsequently, by drawing on the feature evaluation and popularity quantization, MITLP will be implemented within a hybrid B-tree-liked recommending framework, Constrained Location and Social-Trajectory Clustered forest (CLSTC-forest), which can not only produce better performance in practice but also address the facility service constraints. Finally, extensive experiments conducted on real-world datasets demonstrate the higher efficiency and effectiveness of the proposed model.<\/jats:p>","DOI":"10.3233\/ida-205420","type":"journal-article","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T16:02:33Z","timestamp":1631894553000},"page":"1187-1210","source":"Crossref","is-referenced-by-count":3,"title":["Location prediction for facility placement by incorporating multi-characteristic information"],"prefix":"10.1177","volume":"25","author":[{"given":"Pu","family":"Wang","sequence":"first","affiliation":[]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jinjing","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yuyang","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Junhua","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/IDA-205420_ref1","doi-asserted-by":"crossref","unstructured":"J.-G. 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