{"id":"https://openalex.org/W4401864001","doi":"https://doi.org/10.1145/3637528.3671781","title":"Natural Language Explainable Recommendation with Robustness Enhancement","display_name":"Natural Language Explainable Recommendation with Robustness Enhancement","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401864001","doi":"https://doi.org/10.1145/3637528.3671781"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671781","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671781","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5101658564","display_name":"Jingsen Zhang","orcid":"https://orcid.org/0000-0003-2997-3386"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingsen Zhang","raw_affiliation_strings":["Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2997-3386","affiliations":[{"raw_affiliation_string":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043446092","display_name":"Jiakai Tang","orcid":"https://orcid.org/0000-0001-9543-8889"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiakai Tang","raw_affiliation_strings":["Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9543-8889","affiliations":[{"raw_affiliation_string":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101755392","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0003-0144-1775"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Chen","raw_affiliation_strings":["Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0144-1775","affiliations":[{"raw_affiliation_string":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064364239","display_name":"Wenhui Yu","orcid":"https://orcid.org/0000-0002-0886-3543"},"institutions":[{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhui Yu","raw_affiliation_strings":["Kuaishou Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0886-3543","affiliations":[{"raw_affiliation_string":"Kuaishou Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001979415","display_name":"Lantao Hu","orcid":"https://orcid.org/0000-0003-0697-8985"},"institutions":[{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lantao Hu","raw_affiliation_strings":["Kuaishou Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0697-8985","affiliations":[{"raw_affiliation_string":"Kuaishou Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001339397","display_name":"Peng Jiang","orcid":"https://orcid.org/0000-0002-9266-0780"},"institutions":[{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Jiang","raw_affiliation_strings":["Kuaishou Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9266-0780","affiliations":[{"raw_affiliation_string":"Kuaishou Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020615530","display_name":"Han Li","orcid":"https://orcid.org/0009-0000-9801-9292"},"institutions":[{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han Li","raw_affiliation_strings":["Kuaishou Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-9801-9292","affiliations":[{"raw_affiliation_string":"Kuaishou Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101658564"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":1.3245,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83712345,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4203","last_page":"4212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9950000047683716,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9908000230789185,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.817524254322052},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6976968050003052},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5347280502319336},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4249390661716461},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3939957916736603},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.0592198371887207}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.817524254322052},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6976968050003052},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5347280502319336},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4249390661716461},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3939957916736603},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0592198371887207},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671781","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671781","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery 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":39,"referenced_works":["https://openalex.org/W1593271688","https://openalex.org/W2152184085","https://openalex.org/W2165612380","https://openalex.org/W2337403844","https://openalex.org/W2619206542","https://openalex.org/W2733769132","https://openalex.org/W2739992143","https://openalex.org/W2740167620","https://openalex.org/W2798435682","https://openalex.org/W2798713837","https://openalex.org/W2798868970","https://openalex.org/W2801992635","https://openalex.org/W2950275995","https://openalex.org/W2962907114","https://openalex.org/W2966349618","https://openalex.org/W3034190247","https://openalex.org/W3035098003","https://openalex.org/W3094497946","https://openalex.org/W3101023724","https://openalex.org/W3101366597","https://openalex.org/W3113541712","https://openalex.org/W3133706083","https://openalex.org/W3153754021","https://openalex.org/W3195311662","https://openalex.org/W3210519732","https://openalex.org/W3211276971","https://openalex.org/W3212948553","https://openalex.org/W4212774754","https://openalex.org/W4221158409","https://openalex.org/W4284670593","https://openalex.org/W4284687474","https://openalex.org/W4285171841","https://openalex.org/W4360612299","https://openalex.org/W4367047369","https://openalex.org/W4376851389","https://openalex.org/W4385562461","https://openalex.org/W6600686112","https://openalex.org/W6602490798","https://openalex.org/W6803781036"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W3204019825","https://openalex.org/W4226226396","https://openalex.org/W3153750606","https://openalex.org/W4308854837"],"abstract_inverted_index":{"Natural":[0],"language":[1,141,198],"explainable":[2,142],"recommendation":[3,135],"has":[4,32,62],"become":[5],"a":[6,130,201,206],"promising":[7],"direction":[8],"to":[9,23,174,187,211,223],"facilitate":[10],"more":[11],"efficient":[12],"and":[13,82,110,128,167,183,204],"informed":[14],"user":[15],"decisions.":[16],"Previous":[17],"models":[18],"mostly":[19],"focus":[20],"on":[21,108,123,163],"how":[22],"enhance":[24],"the":[25,29,38,57,79,83,99,102,111,134,145,152,176,189,193,213,216,225],"explanation":[26],"accuracy.":[27],"However,":[28],"robustness":[30,58,84,214],"problem":[31,132,203],"been":[33],"largely":[34],"ignored,":[35],"which":[36],"requires":[37],"explanations":[39],"generated":[40,217],"for":[41,86],"similar":[42],"user-item":[43],"pairs":[44],"should":[45,89,104],"not":[46],"be":[47,91,105,119],"too":[48],"much":[49],"different.":[50,92],"Different":[51,67],"from":[52],"traditional":[53],"classification":[54],"problems,":[55],"improving":[56],"of":[59,101,147,171,215,227],"natural":[60,140],"languages":[61],"two":[63],"unique":[64],"characteristics:":[65],"(1)":[66],"token":[68,95,181],"importances,":[69],"that":[70,97,137],"is,":[71,98,138],"different":[72],"tokens":[73],"play":[74],"various":[75],"roles":[76],"in":[77,133,155],"representing":[78],"complete":[80],"sentence,":[81],"requirements":[85],"predicting":[87],"them":[88],"also":[90,118],"(2)":[93],"Continuous":[94],"semantics,":[96,109],"similarity":[100],"output":[103],"judged":[106],"based":[107],"sequences":[112],"without":[113],"any":[114],"token-level":[115],"overlap":[116],"may":[117],"highly":[120],"similar.":[121],"Based":[122],"these":[124],"characteristics,":[125,195],"we":[126,159,196],"formulate":[127],"solve":[129],"novel":[131],"domain,":[136],"robust":[139,165],"recommendation.":[143],"To":[144],"best":[146],"our":[148,161,228],"knowledge,":[149],"it":[150],"is":[151],"first":[153],"time":[154],"this":[156],"field.":[157],"Specifically,":[158],"base":[160],"modeling":[162],"adversarial":[164,177],"optimization":[166],"design":[168,205],"four":[169],"types":[170],"heuristic":[172],"methods":[173],"modify":[175],"outputs":[178],"with":[179],"weighted":[180],"probabilities":[182],"synonym":[184],"replacements.":[185],"Furthermore,":[186],"consider":[188],"mutual":[190],"influence":[191],"between":[192],"above":[194],"regard":[197],"generation":[199],"as":[200],"decision-making":[202],"dual-policy":[207],"reinforcement":[208],"learning":[209],"framework":[210],"improve":[212],"languages.":[218],"We":[219],"conduct":[220],"extensive":[221],"experiments":[222],"demonstrate":[224],"effectiveness":[226],"framework.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}