{"id":"https://openalex.org/W7128229151","doi":"https://doi.org/10.20944/preprints202507.0541.v1","title":"Enhancing Predictive Accuracy in Product Usage Forecasting through a Meta-Learned Attention-Based DeepFM Framework","display_name":"Enhancing Predictive Accuracy in Product Usage Forecasting through a Meta-Learned Attention-Based DeepFM Framework","publication_year":2025,"publication_date":"2025-07-07","ids":{"openalex":"https://openalex.org/W7128229151","doi":"https://doi.org/10.20944/preprints202507.0541.v1"},"language":"en","primary_location":{"id":"doi:10.20944/preprints202507.0541.v1","doi":"https://doi.org/10.20944/preprints202507.0541.v1","is_oa":true,"landing_page_url":"https://doi.org/10.20944/preprints202507.0541.v1","pdf_url":null,"source":{"id":"https://openalex.org/S6309402219","display_name":"Preprints.org","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.20944/preprints202507.0541.v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117510902","display_name":"Owen Graham","orcid":"https://orcid.org/0009-0002-8805-4734","observed_orcids":["https://orcid.org/0009-0002-8805-4734"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Owen Graham","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5117432305","display_name":"Joshua Fredricks","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joshua Fredricks","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5117510902"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.5029000043869019,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.5029000043869019,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.16670000553131104,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.1151999980211258,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/meta-learning","display_name":"meta-learning","score":0.6880000233650208},{"id":"https://openalex.org/keywords/attention-mechanism","display_name":"attention mechanism","score":0.9200000166893005},{"id":"https://openalex.org/keywords/customer-behavior-prediction","display_name":"customer behavior prediction","score":0.7950000166893005},{"id":"https://openalex.org/keywords/curriculum-learning","display_name":"curriculum learning","score":0.9610000252723694},{"id":"https://openalex.org/keywords/data-sparsity","display_name":"data sparsity","score":0.9169999957084656},{"id":"https://openalex.org/keywords/feature-interpretability","display_name":"feature interpretability","score":0.6549999713897705},{"id":"https://openalex.org/keywords/inventory-management","display_name":"inventory management","score":0.8960000276565552},{"id":"https://openalex.org/keywords/user-engagement","display_name":"user engagement","score":0.9470000267028809}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8127999901771545},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8009999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6536999940872192},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.598800003528595},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5559999942779541},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.5141000151634216},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.46540001034736633},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.46480000019073486},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4620000123977661},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4442000091075897},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4156000018119812},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3837999999523163},{"id":"https://openalex.org/C127722929","wikidata":"https://www.wikidata.org/wiki/Q7833714","display_name":"Transaction data","level":3,"score":0.3828999996185303},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37869998812675476},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C23213687","wikidata":"https://www.wikidata.org/wiki/Q301468","display_name":"Consumer behaviour","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C19351080","wikidata":"https://www.wikidata.org/wiki/Q1395034","display_name":"New product development","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.2703999876976013},{"id":"https://openalex.org/C2781353284","wikidata":"https://www.wikidata.org/wiki/Q7901676","display_name":"Usage data","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.2662999927997589},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.20944/preprints202507.0541.v1","doi":"https://doi.org/10.20944/preprints202507.0541.v1","is_oa":true,"landing_page_url":"https://doi.org/10.20944/preprints202507.0541.v1","pdf_url":null,"source":{"id":"https://openalex.org/S6309402219","display_name":"Preprints.org","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.20944/preprints202507.0541.v1","doi":"https://doi.org/10.20944/preprints202507.0541.v1","is_oa":true,"landing_page_url":"https://doi.org/10.20944/preprints202507.0541.v1","pdf_url":null,"source":{"id":"https://openalex.org/S6309402219","display_name":"Preprints.org","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4411855670","https://openalex.org/W4411855343","https://openalex.org/W4410537802","https://openalex.org/W4412084485","https://openalex.org/W4412084344","https://openalex.org/W4412084538","https://openalex.org/W4412095483","https://openalex.org/W4412632640","https://openalex.org/W4415271770","https://openalex.org/W7130535515"],"abstract_inverted_index":{"In":[0],"the":[1,9,54,86,102,125,170,189,199,202,213,222,238,247],"rapidly":[2],"evolving":[3,117],"landscape":[4],"of":[5,56,182,201,225,240,249],"product":[6,47,155,241],"usage":[7,48,156,242],"forecasting,":[8],"ability":[10],"to":[11,42,65,73,93,101,113,135,207,221,255],"accurately":[12],"predict":[13],"consumer":[14,118,216],"behavior":[15],"is":[16,127],"critical":[17],"for":[18,263],"optimizing":[19],"inventory":[20,274],"management,":[21,275],"enhancing":[22,198],"marketing":[23],"strategies,":[24],"and":[25,59,105,116,162,186,204,266,276],"improving":[26,140],"customer":[27,277],"satisfaction.":[28],"This":[29,218],"study":[30,219],"introduces":[31],"a":[32,106,232,260],"novel":[33],"meta-learned":[34,171],"attention-based":[35,172],"Deep":[36],"Factorization":[37],"Machine":[38],"(DeepFM)":[39],"framework":[40,80,174,234],"designed":[41],"enhance":[43],"predictive":[44,178,228,257],"accuracy":[45],"in":[46,144,180,269],"forecasting.":[49,243],"The":[50,79,166,244],"proposed":[51],"approach":[52],"leverages":[53],"strengths":[55],"deep":[57],"learning":[58,123],"factorization":[60],"machines,":[61],"integrating":[62,250],"attention":[63,87,190,251],"mechanisms":[64,252],"dynamically":[66],"focus":[67],"on":[68,82,98,130,212,227],"relevant":[69],"features":[70,96],"while":[71],"adapting":[72],"new":[74],"tasks":[75,132],"through":[76],"meta-learning":[77,107,254],"techniques.":[78],"operates":[81],"two":[83],"primary":[84],"pillars:":[85],"mechanism,":[88],"which":[89],"assigns":[90],"varying":[91],"importance":[92],"different":[94],"input":[95],"based":[97,211],"their":[99],"relevance":[100],"prediction":[103],"task,":[104],"strategy":[108],"that":[109,169,235],"facilitates":[110],"rapid":[111],"adaptation":[112],"diverse":[114],"datasets":[115],"behaviors.":[119],"By":[120],"employing":[121],"curriculum":[122],"principles,":[124],"model":[126,203],"trained":[128],"progressively":[129],"simpler":[131],"before":[133],"advancing":[134],"more":[136],"complex":[137],"scenarios,":[138],"thereby":[139],"its":[141],"generalization":[142],"capabilities":[143],"sparse":[145],"data":[146],"environments.":[147],"Empirical":[148],"validation":[149],"was":[150],"conducted":[151],"using":[152],"multiple":[153],"real-world":[154],"datasets,":[157],"including":[158,272],"e-commerce":[159],"transaction":[160],"records":[161],"user":[163],"engagement":[164],"metrics.":[165],"results":[167],"demonstrate":[168],"DeepFM":[173],"significantly":[175],"outperforms":[176],"traditional":[177],"models":[179],"terms":[181],"accuracy,":[183],"precision,":[184],"recall,":[185],"F1-score.":[187],"Furthermore,":[188],"scores":[191],"provide":[192],"valuable":[193],"insights":[194],"into":[195],"feature":[196],"relevance,":[197],"interpretability":[200],"allowing":[205],"stakeholders":[206],"make":[208],"informed":[209],"decisions":[210],"factors":[214],"driving":[215],"behavior.":[217],"contributes":[220],"growing":[223],"body":[224],"literature":[226],"analytics":[229],"by":[230],"presenting":[231],"robust":[233],"effectively":[236],"addresses":[237],"challenges":[239],"findings":[245],"underscore":[246],"potential":[248],"with":[253],"improve":[256],"performance,":[258],"offering":[259],"promising":[261],"avenue":[262],"future":[264],"research":[265],"practical":[267],"applications":[268],"various":[270],"domains,":[271],"marketing,":[273],"relationship":[278],"management.":[279]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-02-08T00:00:00"}
