Joint model of user check-in activities for point-of-interest recommendation

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.

A Simulation Research Towards Better Leverage of Sales Ranking

As a kind of the most significantly popular information in markets,the sales ranking has great impacts on consumer choice.However,there are few discussions on how sales ranking should be provided to consumers in the literature.This paper aims to answer the following two questions:1)To what extent does the sales ranking influence consumer choices;2)When the sales ranking should be provided to consumers.To do so,this paper first constructs a sales ranking model and then provides detailed simulation experiments to demonstrate the model.The experimental results show that for markets where consumer preferences are dramatically different,such as music and movie markets,sales rankings do not have significant influences on consumer choices and should not be provided to consumers until a large number of early independent consumer choices have been accumulated.But for markets in which consumer preferences are similar,such as markets for official supplies,sales rankings have more influences on consumer choices and should be provided to consumers earlier.Furthermore,an evolution strategy is proposed to ascertain the most suitable sales rankings(characterised by suitable influence strength and suitable release time)for some specified online markets.The comparison results show that the optimized sales rankings not only can help consumers discover higher-quality products but also can improve overall sales.