Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

Abstract

AbstractPoint‐of‐interest (POI) recommendation has always faced serious data sparsity problem. Most of the existing POI recommendation methods do not consider the bidirectional relationship between the user‐preference for locations and the location‐ attractiveness to users in the original dataset when solving the sparsity. For this problem, a point‐of‐interest recommendation method based on bidirectional matrix and deep belief network (POI_DBNBM) is proposed in this paper. Specially, from the perspectives of users and locations, we first construct two different matrices with bidirectional relationship by mapping the original dataset, and we use deep belief network (DBN) with strong learning ability to predict the two matrices. Then, a local–global weight optimization method is developed to weight and fuse the two result matrices obtained by DBN, so as to achieve the more precise user's rating prediction matrix for locations. The abundant experimental studies and evaluations are conducted on FourSquare dataset. Results are encouraging and better than those previously reported on the dataset. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.