With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel o...With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.展开更多
Finding an attribute to explain the relationships between a given pair of entities is valuable in many applications.However,many direct solutions fail,owing to its low precision caused by heavy dependence on text and ...Finding an attribute to explain the relationships between a given pair of entities is valuable in many applications.However,many direct solutions fail,owing to its low precision caused by heavy dependence on text and low recall by evidence scarcity.Thus,we propose a generalization-and-inference framework and implement it to build a system:entity-relationship finder(ERF).Our main idea is conceptualizing entity pairs into proper concept pairs,as intermediate random variables to form the explanation.Although entity conceptualization has been studied,it has new challenges of collective optimization for multiple relationship instances,joint optimization for both entities,and aggregation of diluted observations into the head concepts defining the relationship.We propose conceptualization solutions and validate them as well as the framework with extensive experiments.展开更多
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China(2014BAK15B01)
文摘With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
基金the Shanghai Science and Technology Innovation Action Plan(No.19511120400)the National Key Research and Development Project(No.2020AAA0109302)the Shanghai Municipal Science and Technology Major Project(No.2021SHZDZX0103)。
文摘Finding an attribute to explain the relationships between a given pair of entities is valuable in many applications.However,many direct solutions fail,owing to its low precision caused by heavy dependence on text and low recall by evidence scarcity.Thus,we propose a generalization-and-inference framework and implement it to build a system:entity-relationship finder(ERF).Our main idea is conceptualizing entity pairs into proper concept pairs,as intermediate random variables to form the explanation.Although entity conceptualization has been studied,it has new challenges of collective optimization for multiple relationship instances,joint optimization for both entities,and aggregation of diluted observations into the head concepts defining the relationship.We propose conceptualization solutions and validate them as well as the framework with extensive experiments.
