{"id":"https://openalex.org/W2783691043","doi":"https://doi.org/10.1145/3159652.3159718","title":"Review-Aware Answer Prediction for Product-Related Questions Incorporating Aspects","display_name":"Review-Aware Answer Prediction for Product-Related Questions Incorporating Aspects","publication_year":2018,"publication_date":"2018-02-02","ids":{"openalex":"https://openalex.org/W2783691043","doi":"https://doi.org/10.1145/3159652.3159718","mag":"2783691043"},"language":"en","primary_location":{"id":"doi:10.1145/3159652.3159718","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3159652.3159718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101600341","display_name":"Qian Yu","orcid":"https://orcid.org/0000-0002-0538-7940"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Qian Yu","raw_affiliation_strings":["Chinese University of Hong Kong, Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese University of Hong Kong, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111426224","display_name":"Wai Lam","orcid":null},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wai Lam","raw_affiliation_strings":["Chinese University of Hong Kong, Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese University of Hong Kong, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"691","last_page":"699"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T10028","display_name":"Topic Modeling","score":0.9994000196456909,"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":0.987500011920929,"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":0.7635751962661743},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.6675827503204346},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6411749124526978},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6301400661468506},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5702369213104248},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.5289081335067749},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.46332448720932007},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.42868298292160034},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3669235408306122},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3658526539802551},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.23022779822349548}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7635751962661743},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.6675827503204346},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6411749124526978},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6301400661468506},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5702369213104248},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5289081335067749},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.46332448720932007},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.42868298292160034},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3669235408306122},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3658526539802551},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.23022779822349548},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3159652.3159718","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3159652.3159718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1785350809","https://openalex.org/W1880262756","https://openalex.org/W1966443646","https://openalex.org/W1990457736","https://openalex.org/W1993077754","https://openalex.org/W2022149269","https://openalex.org/W2068223736","https://openalex.org/W2090243146","https://openalex.org/W2093390569","https://openalex.org/W2096259080","https://openalex.org/W2104009457","https://openalex.org/W2129604374","https://openalex.org/W2148721079","https://openalex.org/W2151149636","https://openalex.org/W2181504572","https://openalex.org/W2243869100","https://openalex.org/W2250851590","https://openalex.org/W2251287417","https://openalex.org/W2252136820","https://openalex.org/W2252217313","https://openalex.org/W2270499809","https://openalex.org/W2276398478","https://openalex.org/W2339630617","https://openalex.org/W2341820192","https://openalex.org/W2397663929","https://openalex.org/W2466490286","https://openalex.org/W2468484304","https://openalex.org/W2468672598","https://openalex.org/W2510530102","https://openalex.org/W2516014563","https://openalex.org/W2534686471","https://openalex.org/W2539338396","https://openalex.org/W2561827022","https://openalex.org/W2963662719","https://openalex.org/W2963682631","https://openalex.org/W2964212344","https://openalex.org/W4206765718"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2145836866","https://openalex.org/W2803255133","https://openalex.org/W2909431601"],"abstract_inverted_index":{"In":[0,95,111],"E-commerce":[1],"sites,":[2],"there":[3],"are":[4,30,64,78,81,87],"platforms":[5],"for":[6,67,70,108,128,165,189,212],"users":[7,35],"to":[8,37,97,185],"pose":[9],"product-related":[10,129],"questions":[11,21,32,72,80,192],"and":[12,59,83,92,181],"experienced":[13],"customers":[14],"may":[15],"provide":[16,105,202],"answers":[17,188],"voluntarily.":[18],"Among":[19],"the":[20,42,122,187,230],"asked":[22],"by":[23,206],"users,":[24],"a":[25,46,51,116,149,223],"large":[26],"proportion":[27],"of":[28,85,124,146,154,196,232],"them":[29,86],"yes-no":[31,191],"reflecting":[33],"that":[34,79,119,159],"wish":[36],"know":[38],"whether":[39],"or":[40,49],"not":[41,65],"product":[43,220],"can":[44,104,120,161,201],"satisfy":[45],"certain":[47,52],"criterion":[48],"meet":[50],"expectation.":[53],"Both":[54],"Question":[55,61],"Answering":[56,62],"(QA)":[57],"approaches":[58],"Community":[60],"methods":[63],"suitable":[66],"answer":[68,109,126,170],"prediction":[69,127],"new":[71,117,166,190],"in":[73,135,172],"this":[74,112,136,155],"setting.":[75],"The":[76,131,168],"reasons":[77],"product-associated":[82],"many":[84],"concerned":[88],"about":[89],"user":[90,101],"experiences":[91],"subjective":[93],"opinions.":[94],"addition":[96],"existing":[98,178],"question-answer":[99],"pairs,":[100],"written":[102],"reviews":[103,147,204],"useful":[106],"clues":[107],"prediction.":[110],"paper,":[113],"we":[114],"propose":[115],"framework":[118,137,200],"tackle":[121],"task":[123],"review-aware":[125],"questions.":[130,167],"aspect":[132],"analytics":[133],"model":[134,157,171],"learns":[138],"latent":[139],"aspects":[140,208],"as":[141,143,210],"well":[142],"aspect-specific":[144,163],"embeddings":[145],"via":[148],"3-order":[150],"Autoencoder.":[151],"One":[152],"advantage":[153],"learned":[156,175],"is":[158,183],"it":[160],"generate":[162],"representations":[164],"predictive":[169],"our":[173,199,233],"framework,":[174],"jointly":[176],"from":[177,222],"questions,":[179],"answers,":[180],"reviews,":[182],"able":[184],"predict":[186],"taking":[193],"into":[194],"consideration":[195],"aspects.":[197],"Besides,":[198],"supportive":[203],"grouped":[205],"relevant":[207],"serving":[209],"information":[211],"explainable":[213],"answers.":[214],"Experiment":[215],"results":[216],"on":[217],"15":[218],"different":[219],"categories":[221],"large-scale":[224],"benchmark":[225],"E-commence":[226],"QA":[227],"dataset":[228],"demonstrate":[229],"effectiveness":[231],"framework.":[234]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":11},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}