{"id":"https://openalex.org/W2944883203","doi":"https://doi.org/10.1145/3320326.3320371","title":"Prediction Demand for Classified Ads Using Machine Learning","display_name":"Prediction Demand for Classified Ads Using Machine Learning","publication_year":2019,"publication_date":"2019-03-27","ids":{"openalex":"https://openalex.org/W2944883203","doi":"https://doi.org/10.1145/3320326.3320371","mag":"2944883203"},"language":"en","primary_location":{"id":"doi:10.1145/3320326.3320371","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3320326.3320371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Networking, Information Systems & Security","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/A5037552731","display_name":"Marouane Adnane","orcid":"https://orcid.org/0000-0002-3060-6893"},"institutions":[{"id":"https://openalex.org/I126477371","display_name":"Mohammed V University","ror":"https://ror.org/00r8w8f84","country_code":"MA","type":"education","lineage":["https://openalex.org/I126477371"]}],"countries":["MA"],"is_corresponding":true,"raw_author_name":"Marouane Adnane","raw_affiliation_strings":["IRDA Team, ADMIR Lab, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco"],"affiliations":[{"raw_affiliation_string":"IRDA Team, ADMIR Lab, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco","institution_ids":["https://openalex.org/I126477371"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065169221","display_name":"Mohammed El Haj Tirari","orcid":"https://orcid.org/0000-0001-9217-8882"},"institutions":[{"id":"https://openalex.org/I4210114322","display_name":"Institut National de Statistique et d'Economie Appliqu\u00e9e","ror":"https://ror.org/02g8ke585","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210114322"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Mohammed El","raw_affiliation_strings":["HAJ TIRARI Lab. MASAFEQ INSEA Rabat, Morocco"],"affiliations":[{"raw_affiliation_string":"HAJ TIRARI Lab. MASAFEQ INSEA Rabat, Morocco","institution_ids":["https://openalex.org/I4210114322"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073155664","display_name":"Sanaa El Fkihi","orcid":"https://orcid.org/0000-0002-5255-4406"},"institutions":[{"id":"https://openalex.org/I126477371","display_name":"Mohammed V University","ror":"https://ror.org/00r8w8f84","country_code":"MA","type":"education","lineage":["https://openalex.org/I126477371"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Sanaa El Fkihi","raw_affiliation_strings":["IRDA Team, ADMIR Lab, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco"],"affiliations":[{"raw_affiliation_string":"IRDA Team, ADMIR Lab, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco","institution_ids":["https://openalex.org/I126477371"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037074197","display_name":"Rachid Oulad Haj Thami","orcid":"https://orcid.org/0000-0001-9736-7260"},"institutions":[{"id":"https://openalex.org/I126477371","display_name":"Mohammed V University","ror":"https://ror.org/00r8w8f84","country_code":"MA","type":"education","lineage":["https://openalex.org/I126477371"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Rachid Oulad Haj Thami","raw_affiliation_strings":["IRDA Team, ADMIR Lab, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco"],"affiliations":[{"raw_affiliation_string":"IRDA Team, ADMIR Lab, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco","institution_ids":["https://openalex.org/I126477371"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5037552731"],"corresponding_institution_ids":["https://openalex.org/I126477371"],"apc_list":null,"apc_paid":null,"fwci":0.42,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.6992829,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9922999739646912,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9922999739646912,"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9819999933242798,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/mean-squared-error","display_name":"Mean squared error","score":0.8006116151809692},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7175827622413635},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6693519353866577},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6671667098999023},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5657875537872314},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5529034733772278},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5277974009513855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.518650233745575},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.480866014957428},{"id":"https://openalex.org/keywords/generalization-error","display_name":"Generalization error","score":0.47358494997024536},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.46123290061950684},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.4535754919052124},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4361749589443207},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4003126919269562},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.28756147623062134},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2605525851249695},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20525670051574707},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.11615362763404846}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.8006116151809692},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7175827622413635},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6693519353866577},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6671667098999023},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5657875537872314},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5529034733772278},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5277974009513855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.518650233745575},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.480866014957428},{"id":"https://openalex.org/C117765406","wikidata":"https://www.wikidata.org/wiki/Q5362437","display_name":"Generalization error","level":3,"score":0.47358494997024536},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.46123290061950684},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.4535754919052124},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4361749589443207},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4003126919269562},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.28756147623062134},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2605525851249695},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20525670051574707},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.11615362763404846},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3320326.3320371","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3320326.3320371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Networking, Information Systems & Security","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W128984794","https://openalex.org/W1570448133","https://openalex.org/W1930624869","https://openalex.org/W2056132907","https://openalex.org/W2101234009","https://openalex.org/W2145073242","https://openalex.org/W2174610466","https://openalex.org/W2295598076","https://openalex.org/W2513519930","https://openalex.org/W2565457067","https://openalex.org/W2588655148","https://openalex.org/W2741657101","https://openalex.org/W2759713970","https://openalex.org/W2792986592","https://openalex.org/W2797753066","https://openalex.org/W2809515324","https://openalex.org/W2885442465","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2947069497","https://openalex.org/W2997591727","https://openalex.org/W3085162807","https://openalex.org/W3102476541","https://openalex.org/W3136871849","https://openalex.org/W4230927722","https://openalex.org/W4300546174","https://openalex.org/W4405190226","https://openalex.org/W6602439081","https://openalex.org/W6681651645"],"related_works":["https://openalex.org/W3208169454","https://openalex.org/W2020347745","https://openalex.org/W4298012357","https://openalex.org/W4296079469","https://openalex.org/W2040303975","https://openalex.org/W2896054965","https://openalex.org/W2896110774","https://openalex.org/W2568135170","https://openalex.org/W1535931533","https://openalex.org/W2126089203"],"abstract_inverted_index":{"Classified":[0],"ads":[1,52],"prediction":[2,53,61],"is":[3,117],"a":[4,18],"very":[5],"interesting":[6],"activity":[7],"for":[8,38],"organizations":[9],"in":[10,57],"order":[11,58],"to":[12,59,96,101],"increase":[13],"the":[14,22,34,76,79,98,103,110,118],"purchase":[15],"quantity":[16],"of":[17,24,36,54,78,126],"product":[19],"and":[20,68,74],"thereafter":[21],"possibility":[23],"sale.":[25],"Used":[26],"goods":[27],"predicting":[28],"can":[29],"be":[30],"done":[31],"by":[32],"calculating":[33],"probability":[35],"sale":[37],"each":[39],"selected":[40],"product.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45],"conduct":[46],"an":[47,123],"empirical":[48],"analysis":[49],"on":[50],"classified":[51],"Avito":[55],"dataset":[56],"develop":[60],"models":[62,81,100],"using":[63,82],"three":[64],"individual":[65],"machine-learning":[66],"techniques":[67],"five":[69],"ensemble":[70],"learners.":[71],"We":[72],"compare":[73],"evaluate":[75],"performance":[77],"proposed":[80],"Root":[83],"Mean":[84],"Square":[85],"Error":[86],"(RMSE)":[87],"measure.":[88],"The":[89,106],"stacked":[90],"generalization":[91],"method":[92],"was":[93],"also":[94],"used":[95],"combine":[97],"best-performed":[99],"select":[102],"best":[104],"one.":[105],"results":[107],"show":[108],"that":[109],"Extreme":[111],"Gradient":[112],"Boosting":[113],"Machine":[114],"algorithm":[115],"(XGBoost)":[116],"most":[119],"accurate":[120],"model":[121],"with":[122],"RMSE":[124],"value":[125],"0.2253.":[127]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}