{"id":"https://openalex.org/W7147473323","doi":"https://doi.org/10.48550/arxiv.2603.29259","title":"Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE","display_name":"Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7147473323","doi":"https://doi.org/10.48550/arxiv.2603.29259"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29259","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29259","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.29259","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132543662","display_name":"Hejin Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Hejin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132628244","display_name":"Jusheng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jusheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132596722","display_name":"Kaitong Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Kaitong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132639316","display_name":"Jian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5075012459","display_name":"Rong Pan","orcid":"https://orcid.org/0000-0001-5171-8248"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Rong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.5378999710083008,"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":0.5378999710083008,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.12430000305175781,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.059700001031160355,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.6579999923706055},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5486000180244446},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5400999784469604},{"id":"https://openalex.org/keywords/preference-learning","display_name":"Preference learning","score":0.4745999872684479},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.44749999046325684},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.44040000438690186},{"id":"https://openalex.org/keywords/robust-optimization","display_name":"Robust optimization","score":0.40720000863075256},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.4058000147342682}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7080000042915344},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6579999923706055},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6255999803543091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5877000093460083},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5486000180244446},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5400999784469604},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.4745999872684479},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.44749999046325684},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.44040000438690186},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4277999997138977},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.40720000863075256},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.4058000147342682},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3813999891281128},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C194387892","wikidata":"https://www.wikidata.org/wiki/Q1747770","display_name":"Stochastic optimization","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C73602740","wikidata":"https://www.wikidata.org/wiki/Q7795822","display_name":"Thompson sampling","level":3,"score":0.26969999074935913},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26809999346733093},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29259","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29259","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.29259","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29259","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Preference-based":[0],"alignment":[1],"objectives":[2],"have":[3],"been":[4],"widely":[5],"adopted,":[6],"from":[7,76],"RLHF-style":[8],"pairwise":[9],"learning":[10],"in":[11,18],"large":[12],"language":[13],"models":[14],"to":[15,50,90,127],"emerging":[16],"applications":[17],"recommender":[19],"systems.":[20],"Yet,":[21],"existing":[22],"work":[23],"rarely":[24],"examines":[25],"how":[26],"Direct":[27],"Preference":[28],"Optimization":[29],"(DPO)":[30],"behaves":[31],"under":[32],"implicit":[33],"feedback,":[34],"where":[35],"unobserved":[36],"items":[37],"are":[38],"not":[39],"reliable":[40],"negatives.":[41],"We":[42,86],"conduct":[43],"systematic":[44],"experiments":[45],"on":[46,130],"multimodal":[47],"sequential":[48],"recommendation":[49],"compare":[51],"common":[52],"negative-selection":[53],"strategies":[54],"and":[55,102],"their":[56],"interaction":[57],"with":[58,73,134],"DPO":[59],"training.":[60],"Our":[61],"central":[62],"finding":[63],"is":[64],"that":[65],"a":[66,77],"simple":[67],"modification,":[68],"replacing":[69],"deterministic":[70],"hard":[71,106],"negatives":[72],"stochastic":[74],"sampling":[75],"dynamic":[78],"top-K":[79],"candidate":[80],"pool,":[81],"consistently":[82],"improves":[83],"ranking":[84],"performance.":[85],"attribute":[87],"its":[88],"effectiveness":[89],"two":[91],"factors:":[92],"(1)":[93],"reducing":[94],"erroneous":[95],"suppressive":[96],"gradients":[97],"caused":[98],"by":[99],"false":[100],"negatives,":[101],"(2)":[103],"retaining":[104],"informative":[105],"signals":[107],"while":[108],"smoothing":[109],"optimization":[110],"via":[111],"controlled":[112],"stochasticity.":[113],"With":[114],"an":[115],"optional":[116],"sparse":[117],"Mixture-of-Experts":[118],"encoder":[119],"for":[120],"efficient":[121],"capacity":[122],"scaling,":[123],"RoDPO":[124],"achieves":[125],"up":[126],"5.25%":[128],"NDCG@5":[129],"three":[131],"Amazon":[132],"benchmarks,":[133],"nearly":[135],"unchanged":[136],"inference":[137],"cost.":[138]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}