{"id":"https://openalex.org/W2337233909","doi":"https://doi.org/10.1145/2911451.2911498","title":"Modeling Document Novelty with Neural Tensor Network for Search Result Diversification","display_name":"Modeling Document Novelty with Neural Tensor Network for Search Result Diversification","publication_year":2016,"publication_date":"2016-07-07","ids":{"openalex":"https://openalex.org/W2337233909","doi":"https://doi.org/10.1145/2911451.2911498","mag":"2337233909"},"language":"en","primary_location":{"id":"doi:10.1145/2911451.2911498","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2911451.2911498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","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":null,"display_name":"Long Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Long Xia","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020766468","display_name":"Jun Xu","orcid":"https://orcid.org/0000-0001-7170-111X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Xu","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101616866","display_name":"Yanyan Lan","orcid":"https://orcid.org/0000-0002-7811-3262"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan Lan","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088621320","display_name":"Jiafeng Guo","orcid":"https://orcid.org/0000-0002-9509-8674"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiafeng Guo","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029998682","display_name":"Xueqi Cheng","orcid":"https://orcid.org/0000-0002-5201-8195"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueqi Cheng","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I19820366"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"395","last_page":"404"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9965000152587891,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9965000152587891,"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":0.9950000047683716,"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":0.9901000261306763,"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":0.753821849822998},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.7172037959098816},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6355270743370056},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5568945407867432},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.5299288630485535},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5085350275039673},{"id":"https://openalex.org/keywords/similarity-learning","display_name":"Similarity learning","score":0.4361714720726013},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.4291658103466034},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42363661527633667},{"id":"https://openalex.org/keywords/novelty-detection","display_name":"Novelty detection","score":0.4102631211280823},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36230751872062683},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.323225736618042},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.21525615453720093}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.753821849822998},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.7172037959098816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6355270743370056},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5568945407867432},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.5299288630485535},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5085350275039673},{"id":"https://openalex.org/C2779597229","wikidata":"https://www.wikidata.org/wiki/Q17146505","display_name":"Similarity learning","level":3,"score":0.4361714720726013},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.4291658103466034},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42363661527633667},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.4102631211280823},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36230751872062683},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.323225736618042},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.21525615453720093},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2911451.2911498","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2911451.2911498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G619062981","display_name":null,"funder_award_id":"20144310, 2016102","funder_id":"https://openalex.org/F4320322847","funder_display_name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences"},{"id":"https://openalex.org/G8725540640","display_name":null,"funder_award_id":"61232010, 61232010,61472401, 61433014, 61425016, 61203298","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322847","display_name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","ror":"https://ror.org/031141b54"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1498594556","https://openalex.org/W1966462845","https://openalex.org/W1990473707","https://openalex.org/W1993320088","https://openalex.org/W2005084129","https://openalex.org/W2007815473","https://openalex.org/W2009979684","https://openalex.org/W2023188792","https://openalex.org/W2023599408","https://openalex.org/W2047804176","https://openalex.org/W2047952076","https://openalex.org/W2070740689","https://openalex.org/W2083305840","https://openalex.org/W2088121730","https://openalex.org/W2096658177","https://openalex.org/W2100419635","https://openalex.org/W2103404183","https://openalex.org/W2104373244","https://openalex.org/W2104895009","https://openalex.org/W2107126505","https://openalex.org/W2107743791","https://openalex.org/W2113640060","https://openalex.org/W2127426251","https://openalex.org/W2131744502","https://openalex.org/W2132314908","https://openalex.org/W2147152072","https://openalex.org/W2149427297","https://openalex.org/W2152228468","https://openalex.org/W2157391629","https://openalex.org/W2163200373","https://openalex.org/W2197919320","https://openalex.org/W2604272474","https://openalex.org/W3138773240","https://openalex.org/W4230624213","https://openalex.org/W4233135949"],"related_works":["https://openalex.org/W2064636555","https://openalex.org/W2585503716","https://openalex.org/W1939982668","https://openalex.org/W2105014086","https://openalex.org/W2076090200","https://openalex.org/W3025682415","https://openalex.org/W2081173909","https://openalex.org/W4389009659","https://openalex.org/W198251434","https://openalex.org/W4312933423"],"abstract_inverted_index":{"Search":[0],"result":[1,27],"diversification":[2,28],"has":[3],"attracted":[4],"considerable":[5],"attention":[6],"as":[7],"a":[8,39,73,112,115,133],"means":[9],"to":[10,34,44,87,94,107,152,162,203],"tackle":[11],"the":[12,22,36,48,51,61,64,67,79,83,95,99,109,123,128,139,143,159,167,189,196,199],"ambiguous":[13],"or":[14,126],"multi-faceted":[15],"information":[16],"needs":[17],"of":[18,21,38,75,97,111,120,142],"users.":[19],"One":[20],"key":[23],"problems":[24],"in":[25,90],"search":[26],"is":[29,69],"novelty,":[30],"that":[31,188],"is,":[32],"how":[33],"measure":[35],"novelty":[37,68,110,135],"candidate":[40,144],"document":[41,53,100,113,145],"with":[42,114,176],"respect":[43],"other":[45,147],"documents.":[46,148],"In":[47,63,102],"heuristic":[49],"approaches,":[50,66],"predefined":[52],"similarity":[54,80,124],"functions":[55,81,125,171],"are":[56,85,172],"directly":[57],"utilized":[58],"for":[59],"defining":[60,122],"novelty.":[62,101],"learning":[65,151,161,202],"characterized":[70],"based":[71,137],"on":[72,138,182],"set":[74],"handcrafted":[76],"features.":[77],"Both":[78],"and":[82,146,174],"features":[84],"difficult":[86],"manually":[88,121],"design":[89],"real":[91],"world":[92],"due":[93],"complexity":[96],"modeling":[98],"this":[103],"paper,":[104],"we":[105],"propose":[106],"model":[108,168],"neural":[116],"tensor":[117],"network.":[118],"Instead":[119],"features,":[127],"new":[129,190],"method":[130],"automatically":[131],"learns":[132],"nonlinear":[134],"function":[136],"preliminary":[140],"representation":[141],"New":[149],"diverse":[150],"rank":[153,163,204],"models":[154],"can":[155,193],"be":[156],"derived":[157,191],"under":[158],"relational":[160,201],"framework.":[164],"To":[165],"determine":[166],"parameters,":[169],"loss":[170],"constructed":[173],"optimized":[175],"stochastic":[177],"gradient":[178],"descent.":[179],"Extensive":[180],"experiments":[181],"three":[183],"public":[184],"TREC":[185],"datasets":[186],"show":[187],"algorithms":[192],"significantly":[194],"outperform":[195],"baselines,":[197],"including":[198],"state-of-the-art":[200],"models.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
