Exploring timeliness for accurate recommendation in location-based social networks

Abstract

An individual's location history in the real world implies his or her interests and behaviors. This paper analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.

Improving the design of a recommendation system using evaluation criteria and metrics as a guide

“…Besides accuracy, specificity has also gained deserved attention among researchers (He et al, 2016;Ramalho et al, 2014). Furthermore, timeliness has got a very important place on the list of evaluation metrics for recommendation systems in health care and other healthrelated systems (Caamares and Castells, 2018;Fong et al, 2011;O'Mahony and Smyth, 2010;Wolfe and Zhang, 2010;Xu et al, 2018;Zhang et al, 2018). Adaptability as an evaluation metric for recommendation systems in health care has also featured prominently in literature (Nathanson et al, 2007;Wiesner and Pfeifer, 2014;Wu et al, 2012;Yang et al, 2018).…”