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Knowl. Discov. Data"],"published-print":{"date-parts":[[2015,4,13]]},"abstract":"<jats:p>\n            This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items; however, LA-LDA supports three classes of location-based ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top-\n            <jats:italic>k<\/jats:italic>\n            recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top-\n            <jats:italic>k<\/jats:italic>\n            recommendation and the cold start problem.\n          <\/jats:p>","DOI":"10.1145\/2663356","type":"journal-article","created":{"date-parts":[[2015,4,3]],"date-time":"2015-04-03T20:29:44Z","timestamp":1428092984000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":101,"title":["Modeling Location-Based User Rating Profiles for Personalized Recommendation"],"prefix":"10.1145","volume":"9","author":[{"given":"Hongzhi","family":"Yin","sequence":"first","affiliation":[{"name":"University of Queensland, QLD, Australia"}]},{"given":"Bin","family":"Cui","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Technology, Sydney"}]},{"given":"Zhiting","family":"Hu","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Chengqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Technology, Sydney"}]}],"member":"320","published-online":{"date-parts":[[2015,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2424321.2424348"},{"key":"e_1_2_1_3_1","volume-title":"Mokbel","author":"Bao Jie","year":"2013","unstructured":"Jie Bao , Yu Zheng , David Wilkie , and Mohamed F . 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