Where you like to go next: successive point-of-interest recommendation
Impact in
- Transportation 102
Classified as
- Authors
- Cheng ChenHaiqin YangMichael Rung-Tsong LyuIrwin King
- Journal
- International Joint Conference on Artificial Intelligence
In The Last Decade
doi.org/w1546409232 →Countries where authors are citing Where you like to go next: successive point-of-interest recommendation
This map shows the geographic impact of Where you like to go next: successive point-of-interest recommendation. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Where you like to go next: successive point-of-interest recommendation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Where you like to go next: successive point-of-interest recommendation more than expected).
Fields of papers citing Where you like to go next: successive point-of-interest recommendation
This network shows the impact of Where you like to go next: successive point-of-interest recommendation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Where you like to go next: successive point-of-interest recommendation.
About Where you like to go next: successive point-of-interest recommendation
This paper, published in 2013, received 393 indexed citations . Written by Cheng Chen, Haiqin Yang, Michael Rung-Tsong Lyu and Irwin King covering the research area of Information Systems. It is primarily cited by scholars working on Information Systems (298 citations), Transportation (102 citations), Signal Processing (41 citations), Artificial Intelligence (18 citations) and Building and Construction (12 citations). Published in International Joint Conference on Artificial Intelligence.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w1546409232.