{"type":"ScholarlyArticle","title":"Learning User's Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach","authors":[{"name":"Huayu Li","slug":"huayu-li","given":"Huayu","family":"Li","affiliation":"University of North Carolina at Charlotte"},{"name":"Yong Ge","slug":"yong-ge","given":"Yong","family":"Ge","affiliation":"Nanjing University of Finance and Economic"},{"name":"Defu Lian","slug":"defu-lian","given":"Defu","family":"Lian","affiliation":"Big Data Research Center , University of Electronic Science and Technology of China"},{"name":"Hao Liu","slug":"hao-liu","given":"Hao","family":"Liu","affiliation":"University of North Carolina at Charlotte"}],"abstract":"Point-of-Interest (POI) recommendation has been an important service on location-based social networks. However, it is very challenging to generate accurate recommendations due to the complex nature of user's interest in POI and the data sparseness. In this paper, we propose a novel unified approach that could effectively learn fine-grained and interpretable user's interest, and adaptively model the missing data. Specifically, a user's general interest in POI is modeled as a mixture of her intrinsic and extrinsic interests, upon which we formulate the ranking constraints in our unified recommendation approach. Furthermore, a self-adaptive location-oriented method is proposed to capture the inherent property of missing data, which is formulated as squared error based loss in our unified optimization objective. Extensive experiments on real-world datasets demonstrate the effectiveness and advantage of our approach.","doi":"10.24963/ijcai.2017/294","url":"https://ijcai.org/proceedings/2017/294","pdf":"https://ijcai.org/proceedings/2017/0294.pdf","markdown":"https://ijcai.org/proceedings/2017/294.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/294","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra, IIIA-CSIC","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":294,"pages":{"first":2117,"last":2123},"keywords":["Multidisciplinary Topics and Applications: Personalization and User Modeling","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Balakrishnan and Chopra, 2012] Suhrid Balakrishnan and Sumit Chopra. 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