{"id":"https://openalex.org/W4381191914","doi":"https://doi.org/10.1145/3565472.3592957","title":"Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question Answering","display_name":"Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question Answering","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4381191914","doi":"https://doi.org/10.1145/3565472.3592957"},"language":"en","primary_location":{"id":"doi:10.1145/3565472.3592957","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3565472.3592957","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","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/A5087822107","display_name":"Vaibhav Krishna","orcid":"https://orcid.org/0000-0002-4701-4624"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Vaibhav Krishna","raw_affiliation_strings":["ETH Zurich, Switzerland"],"raw_orcid":"https://orcid.org/0000-0002-4701-4624","affiliations":[{"raw_affiliation_string":"ETH Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057217842","display_name":"Nino Antulov-Fantulin","orcid":"https://orcid.org/0000-0002-4337-2475"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Nino Antulov-Fantulin","raw_affiliation_strings":["Computational Social Science, ETH Zurich, Switzerland"],"raw_orcid":"https://orcid.org/0000-0002-4337-2475","affiliations":[{"raw_affiliation_string":"Computational Social Science, ETH Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5087822107"],"corresponding_institution_ids":["https://openalex.org/I35440088"],"apc_list":null,"apc_paid":null,"fwci":3.4992,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.93422269,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"156","last_page":"163"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13274","display_name":"Expert finding and Q&A systems","score":1.0,"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/T13274","display_name":"Expert finding and Q&A systems","score":1.0,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.991100013256073,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.8134409189224243},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.7266720533370972},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6135203838348389},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.5421844124794006},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49857020378112793},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47444188594818115},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4650242328643799},{"id":"https://openalex.org/keywords/cold-start","display_name":"Cold start (automotive)","score":0.4636038541793823},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.4511593282222748},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.44162479043006897},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.434466689825058},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.41534876823425293},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4065246284008026},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3385600447654724},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1630047857761383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8134409189224243},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.7266720533370972},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6135203838348389},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.5421844124794006},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49857020378112793},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47444188594818115},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4650242328643799},{"id":"https://openalex.org/C2778956030","wikidata":"https://www.wikidata.org/wiki/Q5142477","display_name":"Cold start (automotive)","level":2,"score":0.4636038541793823},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.4511593282222748},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.44162479043006897},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.434466689825058},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.41534876823425293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4065246284008026},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3385600447654724},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1630047857761383},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3565472.3592957","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3565472.3592957","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[{"id":"https://openalex.org/G8274803949","display_name":null,"funder_award_id":"871042","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1925528506","https://openalex.org/W1976727267","https://openalex.org/W1999298115","https://openalex.org/W2000389083","https://openalex.org/W2003570745","https://openalex.org/W2022149269","https://openalex.org/W2071983952","https://openalex.org/W2072910094","https://openalex.org/W2075196988","https://openalex.org/W2078784669","https://openalex.org/W2102956348","https://openalex.org/W2111094216","https://openalex.org/W2119523409","https://openalex.org/W2125189744","https://openalex.org/W2132613313","https://openalex.org/W2138621811","https://openalex.org/W2142281120","https://openalex.org/W2152675241","https://openalex.org/W2342901387","https://openalex.org/W2417541502","https://openalex.org/W2514017751","https://openalex.org/W2514077680","https://openalex.org/W2611228204","https://openalex.org/W2884757772","https://openalex.org/W2892240940","https://openalex.org/W2981720469","https://openalex.org/W2997728017","https://openalex.org/W4306317225","https://openalex.org/W4307124515"],"related_works":["https://openalex.org/W2497939785","https://openalex.org/W2219931199","https://openalex.org/W4241927574","https://openalex.org/W2735929803","https://openalex.org/W2971083348","https://openalex.org/W584290403","https://openalex.org/W3214288750","https://openalex.org/W2786642545","https://openalex.org/W3095646726","https://openalex.org/W2084560547"],"abstract_inverted_index":{"Community":[0],"Question":[1],"Answering":[2],"(CQA)":[3],"websites":[4],"are":[5],"valuable":[6],"knowledge":[7],"repositories":[8],"where":[9],"individuals":[10],"exchange":[11],"information":[12,119],"by":[13],"asking":[14],"and":[15,24,27,61,97,102,117,125],"answering":[16],"questions.":[17,47],"With":[18],"an":[19,163],"ever-increasing":[20],"number":[21],"of":[22,29,114,165],"questions":[23],"high":[25],"in-flow":[26],"out-flow":[28],"users":[30,152],"in":[31,111],"these":[32],"communities,":[33],"a":[34,81,155],"key":[35],"challenge":[36],"is":[37],"to":[38,120,170],"design":[39],"effective":[40],"strategies":[41],"for":[42,45,73,88],"recommending":[43],"experts":[44],"new":[46],"This":[48],"requires":[49],"robust":[50],"approaches":[51],"that":[52,90,141],"facilitate":[53],"modeling":[54],"users\u2019":[55,109],"expertise":[56,110],"given":[57],"their":[58,122],"changing":[59,123],"interests":[60,124],"sparse":[62],"historical":[63,157],"data,":[64],"at":[65],"the":[66,112,136,171],"same":[67],"time":[68],"being":[69],"computationally":[70],"less":[71],"expensive":[72],"periodic":[74],"updates.":[75],"In":[76],"this":[77],"paper,":[78],"we":[79],"propose":[80],"simple":[82],"graph":[83],"diffusion-based":[84],"expert":[85],"recommendation":[86],"model":[87,161],"CQA,":[89],"can":[91],"outperform":[92],"state-of-the-art":[93],"convolutional":[94],"neural":[95],"network":[96,139],"transformers-based":[98],"deep":[99],"learning":[100],"representatives":[101],"collaborative":[103],"models.":[104],"Our":[105],"proposed":[106],"method":[107],"learns":[108],"context":[113],"both":[115],"semantic":[116],"temporal":[118],"capture":[121],"activity":[126],"levels":[127],"with":[128,154],"time.":[129],"Experiments":[130],"on":[131,150],"six":[132],"real-world":[133],"datasets":[134],"from":[135],"Stack":[137],"Exchange":[138],"demonstrate":[140],"our":[142,160],"approach":[143],"outperforms":[144],"competitive":[145],"baseline":[146,173],"methods.":[147],"Further,":[148],"experiments":[149],"cold-start":[151],"(users":[153],"limited":[156],"record)":[158],"show":[159],"achieves":[162],"average":[164],"50%":[166],"performance":[167],"gain":[168],"compared":[169],"best":[172],"method.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-23T08:51:43.019350","created_date":"2025-10-10T00:00:00"}
