{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:17:40Z","timestamp":1761164260423,"version":"build-2065373602"},"reference-count":5,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2016,2,24]],"date-time":"2016-02-24T00:00:00Z","timestamp":1456272000000},"content-version":"vor","delay-in-days":419,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["asistdl.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Proc. Assoc. Info. Sci. Tech."],"published-print":{"date-parts":[[2015,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Heterogeneous graph based information recommendation have been proved useful in recent studies. Given a heterogeneous graph scheme, there are many possible meta paths between the query node and the result node, and each meta path addresses a hypothesis\u2010based ranking function. In prior researches, meta paths are manually selected by domain experts. However, when the graph scheme becomes complex, this method can be inefficient. In this study, we propose feature generation tree, a novel feature selection method for heterogeneous graph mining based recommendation algorithms, which adds graph structure information into the original \u201cfeature selection for ranking\u201d algorithm and saves a fair amount of time for feature computation. In our preliminary experiment, the proposed method outperforms the original \u201cfeature selection for ranking\u201d algorithm in both efficiency and effectiveness.<\/jats:p>","DOI":"10.1002\/pra2.2015.1450520100119","type":"journal-article","created":{"date-parts":[[2016,2,27]],"date-time":"2016-02-27T05:56:50Z","timestamp":1456552610000},"page":"1-4","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Feature selection on heterogeneous graph"],"prefix":"10.1002","volume":"52","author":[{"given":"Chun","family":"Guo","sequence":"first","affiliation":[{"name":"School of Informatics and Computing Indiana University Bloomington  Bloomington IN USA 47405"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaozhong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Informatics and Computing Indiana University Bloomington  Bloomington IN USA 47405"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2016,2,24]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_6_2_1","DOI":"10.1145\/1277741.1277811"},{"doi-asserted-by":"publisher","key":"e_1_2_6_3_1","DOI":"10.1145\/2766462.2767808"},{"doi-asserted-by":"publisher","key":"e_1_2_6_4_1","DOI":"10.1145\/2661829.2661965"},{"doi-asserted-by":"crossref","unstructured":"Liu X. Yu Y. Guo C. Sun Y. &Gao L.(2014).Full\u2010text based context\u2010rich heterogeneous network mining approach for citation recommendation. InProceedings of the 14th ACM\/IEEE\u2010CS Joint Conference on Digital Libraries(pp.361\u2010370). London UK.","key":"e_1_2_6_5_1","DOI":"10.1109\/JCDL.2014.6970191"},{"doi-asserted-by":"publisher","key":"e_1_2_6_6_1","DOI":"10.1145\/1390334.1390435"}],"container-title":["Proceedings of the Association for Information Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fpra2.2015.1450520100119","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fpra2.2015.1450520100119","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/pra2.2015.1450520100119","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/pra2.2015.1450520100119","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/asistdl.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/pra2.2015.1450520100119","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T18:02:00Z","timestamp":1761069720000},"score":1,"resource":{"primary":{"URL":"https:\/\/asistdl.onlinelibrary.wiley.com\/doi\/10.1002\/pra2.2015.1450520100119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,1]]},"references-count":5,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2015,1]]}},"alternative-id":["10.1002\/pra2.2015.1450520100119"],"URL":"https:\/\/doi.org\/10.1002\/pra2.2015.1450520100119","archive":["Portico"],"relation":{},"ISSN":["2373-9231","2373-9231"],"issn-type":[{"type":"print","value":"2373-9231"},{"type":"electronic","value":"2373-9231"}],"subject":[],"published":{"date-parts":[[2015,1]]},"assertion":[{"value":"2016-02-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}