Name
Abstract | ||
|---|---|---|
POI recommendation provides users personalized location recommendation. It helps users to explore new locations and filter uninteresting places that do not match with their interests. Multiple factors influence users to choose a POI, such as useru0027s categorical preferences, temporal activities and location preferences as well as popularity of a POI. In this work, we define a unified framework that takes all these factors into consideration. None of the previous POI recommendation systems consider all four factors: Personal preferences, spatial (location) preferences, temporal influences and POI popularity. This method aims to provide users with a list of recommendation of POIs within a geo-spatial range that should match with their temporal activities and categorical preferences. Experimental results on real-world data show that the proposed recommendation framework outperforms the baseline approaches. |
Year | Venue | Field |
|---|---|---|
2016 | FLAIRS Conference | Recommender system,World Wide Web,Collaborative filtering,Categorical variable,Computer science,Popularity |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Madhuri Debnath | 1 | 11 | 2.74 |
| Praveen Kumar Tripathi | 2 | 179 | 11.83 |
| Ramez Elmasri | 3 | 1950 | 756.86 |