A hybrid recommender system using topic modeling and prefixspan algorithm in social media

Abstract

AbstractRoute schema is difficult to plan for tourists, because they demand to pick points of interest (POI) in unknown areas that align with their preferences and limitations. This research proposes a novel personalized method for POI route recommendation that employs contextual data. The proposed approach enhances the existing methods by considering user preferences and multifaceted tourism contexts. Due to the sparsity of the data, the proposed method employs two-level clustering (DBSCAN based on the Manhattan distance) that reduces the time to discover POI. In specific, this approach utilizes the following: first, a topic pattern model is employed to discover the users’ attraction diffusion while improving the user–user similarity model using a novel asymmetric schema. Second, it has used explicit demographic information to alleviate the cold start issue, and third, it proposes a new strategy for assessing user preferences and also combined the context parameters in the form of a vector model with the Term Frequency Inverse Document Frequency technique to find contexts’ similarity. Furthermore, our framework discovers a list of optimal candidate trips by involving personalized POIs in sequential patterns’ mining (SPM); also, it used an adjusted forgotten function to involve the date context of each trip. Based on two datasets (Flickr and Gowalla), our methodology beats other prior approaches in F-score, RMSE, MAP, and NDCG factors in the experimental evaluation.