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Data management / Recommender systems / Collective intelligence / Collaboration / Information retrieval / Collaborative filtering / GroupLens Research / Database / Relational model / Information science / Database management systems / Computing


RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures Justin J. Levandoski Ahmed Eldawy Michael D. Ekstrand
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Document Date: 2012-03-05 10:35:41


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File Size: 518,76 KB

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City

Model Building Item / Seattle / Keyword / /

Company

Amazon / Hybrid Recommender Systems / Netflix / Google / Collaborative Filtering Recommender Systems / Facebook / The times / Recommender Systems / Collaborative Filtering / Benchmarking Database Systems / Efficient Online Recommender Systems / Pearson / /

Currency

USD / /

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Facility

We store / Model building / University of Minnesota / /

IndustryTerm

text-search results / higher processing time / text-search scores / free text search / query processing / text search / free-text search / text-search subquery acts / real-life e-commerce recommendation scenario / large search space / query execution infrastructure / text search integration / virtual memory paging algorithm / text-search / realworld e-commerce contexts / query processor / hand-built systems / e-commerce scenarios / text-search score / e-commerce scenario / dot-products / text search score / model-build algorithm / model building algorithm / search features / text-search constraints / outside systems / query processing strategies / Internet Computing / real movie recommendation applications / open-source solutions / model builder / data management / text-search query / e-commerce site / software stacks / text-search engines / recommendation generation algorithms / rental site / query-intensive online environments / computing / /

Movie

U / /

OperatingSystem

Ubuntu / /

Organization

University of Minnesota / Minneapolis / VLDB Endowment / Recommender System Architectures Justin J. Levandoski Ahmed Eldawy Michael D. Ekstrand Mohamed F. Mokbel Michael J. Ludwig John T. Riedl Department of Computer Science and Engineering / /

Person

Item / John T. Riedl / J. Ludwig John / Justin J. Levandoski Ahmed Eldawy Michael / Mohamed F. Mokbel Michael / /

Position

recommender model / while still supporting efficient recommendation queries / actor / director / general data management platform / Candidate / /

Product

PostgreSQL 8.4 / MultiLens / PostgreSQL / /

ProgrammingLanguage

Java / SQL / XML / php / /

ProvinceOrState

Minnesota / Washington / /

PublishedMedium

the Internet (Google News / /

Technology

XML / RAM / relational database / 70000 35000 processor / MultiLens algorithm / query processor / The algorithm / virtual memory paging algorithm / shared memory / DBMS / Filtering Recommendation Algorithms / The model-build algorithm / Java / database management system / recommendation generation algorithms / caching / model building algorithm / SCSI / /

URL

http /

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