{"id":"https://openalex.org/W4400525923","doi":"https://doi.org/10.1145/3626772.3657953","title":"Memory-Efficient Deep Recommender Systems using Approximate Rotary Compositional Embedding","display_name":"Memory-Efficient Deep Recommender Systems using Approximate Rotary Compositional Embedding","publication_year":2024,"publication_date":"2024-07-10","ids":{"openalex":"https://openalex.org/W4400525923","doi":"https://doi.org/10.1145/3626772.3657953"},"language":"en","primary_location":{"id":"doi:10.1145/3626772.3657953","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3626772.3657953","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3626772.3657953?download=true","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th 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":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3626772.3657953?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033356396","display_name":"Dongning Ma","orcid":"https://orcid.org/0000-0002-1879-4406"},"institutions":[{"id":"https://openalex.org/I7863295","display_name":"Villanova University","ror":"https://ror.org/02g7kd627","country_code":"US","type":"education","lineage":["https://openalex.org/I7863295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongning Ma","raw_affiliation_strings":["Villanova University, Villanova, PA, USA"],"raw_orcid":"https://orcid.org/0000-0002-1879-4406","affiliations":[{"raw_affiliation_string":"Villanova University, Villanova, PA, USA","institution_ids":["https://openalex.org/I7863295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016146461","display_name":"Xun Jiao","orcid":"https://orcid.org/0000-0003-4476-2501"},"institutions":[{"id":"https://openalex.org/I7863295","display_name":"Villanova University","ror":"https://ror.org/02g7kd627","country_code":"US","type":"education","lineage":["https://openalex.org/I7863295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xun Jiao","raw_affiliation_strings":["Villanova University, Villanova, PA, USA"],"raw_orcid":"https://orcid.org/0000-0003-4476-2501","affiliations":[{"raw_affiliation_string":"Villanova University, Villanova, PA, USA","institution_ids":["https://openalex.org/I7863295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I7863295"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12614871,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2507","last_page":"2511"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9958000183105469,"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"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9810000061988831,"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/computer-science","display_name":"Computer science","score":0.7590253949165344},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.7411342859268188},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6750035285949707},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.5657851696014404},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3617006838321686},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3612545132637024},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3380810022354126},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.25290733575820923}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7590253949165344},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7411342859268188},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6750035285949707},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.5657851696014404},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3617006838321686},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3612545132637024},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3380810022354126},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.25290733575820923}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3626772.3657953","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3626772.3657953","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3626772.3657953?download=true","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3626772.3657953","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3626772.3657953","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3626772.3657953?download=true","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4400525923.pdf","grobid_xml":"https://content.openalex.org/works/W4400525923.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2793768763","https://openalex.org/W2972269283","https://openalex.org/W3104030692","https://openalex.org/W3169936356","https://openalex.org/W3197870239","https://openalex.org/W3210344344","https://openalex.org/W4206914189","https://openalex.org/W4226150639","https://openalex.org/W4320908195","https://openalex.org/W4387846361","https://openalex.org/W6859320503"],"related_works":["https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W2150182025","https://openalex.org/W3092950680","https://openalex.org/W3197542405","https://openalex.org/W2056712470","https://openalex.org/W3125580266","https://openalex.org/W2051487156","https://openalex.org/W4317039510","https://openalex.org/W2932872266"],"abstract_inverted_index":{"Embedding":[0,28],"tables":[1,53,108],"in":[2,109],"deep":[3],"recommender":[4],"systems":[5],"(DRS)":[6],"process":[7],"categorical":[8],"data,":[9],"which":[10,30],"can":[11,101],"be":[12],"memory-intensive":[13],"due":[14],"to":[15,35,49],"the":[16,38,41,66,103,122],"high":[17],"feature":[18],"cardinality.":[19],"In":[20],"this":[21,70],"paper,":[22],"we":[23],"propose":[24],"Approximate":[25],"Rotary":[26],"Compositional":[27],"(ARCE),":[29],"intentionally":[31],"trades":[32],"off":[33],"performance":[34,67,119],"aggressively":[36],"reduce":[37,102],"size":[39],"of":[40,69,106,124],"embedding":[42,48,52,107],"tables.":[43],"Specifically,":[44],"ARCE":[45,72,100,126,135],"uses":[46],"compositional":[47],"split":[50],"large":[51],"into":[54,82],"smaller":[55],"compositions":[56],"and":[57],"replaces":[58],"index":[59,79],"look-ups":[60],"with":[61,115],"vector":[62],"rotations.":[63],"To":[64],"regain":[65],"loss":[68],"trade-off,":[71],"features":[73],"an":[74],"input":[75],"approximation":[76],"where":[77],"one":[78],"is":[80],"mapped":[81],"multiple":[83],"indices,":[84],"creating":[85],"a":[86,90],"larger":[87],"space":[88],"for":[89,127],"potential":[91,123],"increased":[92],"learning":[93],"capability.":[94],"Experimental":[95],"results":[96],"show":[97],"that":[98],"using":[99,125],"memory":[104,129],"overhead":[105],"DRS":[110,131],"by":[111],"more":[112],"than":[113,117],"1000x":[114],"less":[116,128],"3%":[118],"loss,":[120],"highlighting":[121],"intensive":[130],"designs.":[132],"We":[133],"open-source":[134],"at":[136],"https://github.com/VU-DETAIL/arce.":[137]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
