{"id":"https://openalex.org/W4388738145","doi":"https://doi.org/10.1145/3606043.3606047","title":"Semantic Deep Mining Method for Recommendation Based on Small-World Graphs","display_name":"Semantic Deep Mining Method for Recommendation Based on Small-World Graphs","publication_year":2023,"publication_date":"2023-06-17","ids":{"openalex":"https://openalex.org/W4388738145","doi":"https://doi.org/10.1145/3606043.3606047"},"language":"en","primary_location":{"id":"doi:10.1145/3606043.3606047","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3606043.3606047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on High Performance Compilation, Computing and Communications","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100727331","display_name":"Ling Wang","orcid":"https://orcid.org/0000-0001-8956-0511"},"institutions":[{"id":"https://openalex.org/I179060312","display_name":"Northeast Electric Power University","ror":"https://ror.org/00zqaxa34","country_code":"CN","type":"education","lineage":["https://openalex.org/I179060312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Wang","raw_affiliation_strings":["School of Computer Science, Northeast Electric Power University, China"],"raw_orcid":"https://orcid.org/0000-0001-8956-0511","affiliations":[{"raw_affiliation_string":"School of Computer Science, Northeast Electric Power University, China","institution_ids":["https://openalex.org/I179060312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007596695","display_name":"Zihan Yu","orcid":"https://orcid.org/0009-0004-6406-2339"},"institutions":[{"id":"https://openalex.org/I179060312","display_name":"Northeast Electric Power University","ror":"https://ror.org/00zqaxa34","country_code":"CN","type":"education","lineage":["https://openalex.org/I179060312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihan Yu","raw_affiliation_strings":["School of Computer Science, Northeast Electric Power University, China"],"raw_orcid":"https://orcid.org/0009-0004-6406-2339","affiliations":[{"raw_affiliation_string":"School of Computer Science, Northeast Electric Power University, China","institution_ids":["https://openalex.org/I179060312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103133847","display_name":"Yu Lu","orcid":"https://orcid.org/0009-0002-4201-1966"},"institutions":[{"id":"https://openalex.org/I179060312","display_name":"Northeast Electric Power University","ror":"https://ror.org/00zqaxa34","country_code":"CN","type":"education","lineage":["https://openalex.org/I179060312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Lu","raw_affiliation_strings":["School of Computer Science, Northeast Electric Power University, China"],"raw_orcid":"https://orcid.org/0009-0002-4201-1966","affiliations":[{"raw_affiliation_string":"School of Computer Science, Northeast Electric Power University, China","institution_ids":["https://openalex.org/I179060312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074232429","display_name":"Tie Hua Zhou","orcid":"https://orcid.org/0000-0002-3258-0244"},"institutions":[{"id":"https://openalex.org/I179060312","display_name":"Northeast Electric Power University","ror":"https://ror.org/00zqaxa34","country_code":"CN","type":"education","lineage":["https://openalex.org/I179060312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tie Hua Zhou","raw_affiliation_strings":["School of Computer Science, Northeast Electric Power University, China"],"raw_orcid":"https://orcid.org/0000-0002-3258-0244","affiliations":[{"raw_affiliation_string":"School of Computer Science, Northeast Electric Power University, China","institution_ids":["https://openalex.org/I179060312"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26032203,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"21","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9743000268936157,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8252592086791992},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.5509280562400818},{"id":"https://openalex.org/keywords/directory","display_name":"Directory","score":0.5366740822792053},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5149515271186829},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.511111319065094},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.508044958114624},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43604621291160583},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.27594488859176636},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2459067702293396}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8252592086791992},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.5509280562400818},{"id":"https://openalex.org/C2777683733","wikidata":"https://www.wikidata.org/wiki/Q201456","display_name":"Directory","level":2,"score":0.5366740822792053},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5149515271186829},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.511111319065094},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.508044958114624},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43604621291160583},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.27594488859176636},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2459067702293396},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3606043.3606047","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3606043.3606047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on High Performance Compilation, Computing and Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2086179657","https://openalex.org/W2187438542","https://openalex.org/W2284926216","https://openalex.org/W2736209431","https://openalex.org/W2801992635","https://openalex.org/W2899100622","https://openalex.org/W2945818642","https://openalex.org/W2991471644","https://openalex.org/W3008370402","https://openalex.org/W3038664881","https://openalex.org/W3044197330","https://openalex.org/W3048184864","https://openalex.org/W3094675384","https://openalex.org/W3108477921","https://openalex.org/W3134041167","https://openalex.org/W4242735564","https://openalex.org/W4311993042","https://openalex.org/W6784881703"],"related_works":["https://openalex.org/W3115906952","https://openalex.org/W3134811395","https://openalex.org/W3155196058","https://openalex.org/W4252960523","https://openalex.org/W1529821365","https://openalex.org/W2135486207","https://openalex.org/W1499611046","https://openalex.org/W3024420453","https://openalex.org/W2388608119","https://openalex.org/W2915494741"],"abstract_inverted_index":{"Currently,":[0],"the":[1,17,49,55,116,119,124,135,151,160,169,177,187,190,206],"mainstream":[2],"recommendation":[3,97],"techniques":[4],"mainly":[5],"include":[6],"categorized":[7,28],"directory,":[8],"keyword":[9],"search,":[10],"and":[11,19,62,81,110,158,200],"collaborative":[12,72],"filtering.":[13],"However,":[14],"due":[15],"to":[16,33,150,153,162,168,175,185],"complex":[18],"non-uniform":[20],"classification":[21],"rules":[22],"of":[23,27,57,69,123,189,194,205],"various":[24],"information,":[25],"users":[26],"directory":[29],"methods":[30],"are":[31,146,156,183],"prone":[32],"problems":[34,76],"such":[35,77],"as":[36,78],"too":[37,40],"large":[38],"or":[39,66],"small":[41],"a":[42,93,101,111],"range.":[43],"The":[44,203],"keyword-based":[45],"search":[46,195,197],"techniques,":[47,74],"on":[48,106,115],"other":[50,64,67],"hand,":[51],"can":[52],"hardly":[53],"avoid":[54],"dilemma":[56],"falling":[58],"into":[59,134],"local":[60],"information":[61],"ignoring":[63],"keywords":[65],"fields":[68],"information.":[70],"For":[71],"filtering":[73],"again,":[75],"\"cold":[79],"start\"":[80],"\"Matthew":[82],"effect\"":[83],"cannot":[84],"be":[85,154,163],"avoided.":[86],"Therefore,":[87],"in":[88,192,210],"this":[89,211],"paper,":[90],"we":[91],"propose":[92],"semantic":[94,112],"deep":[95],"mining-based":[96],"method":[98,104,191,208],"HRS,":[99],"using":[100],"vector":[102],"retrieval":[103],"based":[105,114],"navigable":[107,136],"small-world":[108,137],"graphs,":[109],"model":[113],"data":[117,131],"from":[118],"technical":[120],"specialized":[121],"lexicon":[122],"constructed":[125],"requirement":[126],"knowledge":[127],"base.":[128],"When":[129],"node":[130,139,152,161,178],"is":[132,141,166,213],"inserted":[133,155],"graph,":[138],"lookup":[140],"performed,":[142],"i.e.,":[143],"nodes":[144],"that":[145],"close":[147,172],"nearest":[148,173],"neighbors":[149,174],"found,":[157],"subsequently":[159],"looked":[164],"up":[165],"connected":[167],"first":[170],"n":[171],"complete":[176],"insertion.":[179],"Finally,":[180],"experimental":[181],"results":[182],"used":[184],"compare":[186],"effectiveness":[188],"terms":[193],"accuracy,":[196],"completion":[198],"rate":[199],"F1":[201],"value.":[202],"reliability":[204],"recommended":[207],"proposed":[209],"topic":[212],"demonstrated":[214],"through":[215],"experiments.":[216]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}