Agent learning of ML latest-small dataset across various parameters.
<p>Agent learning of ML latest-small dataset across various parameters.</p>
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1852022572419383296 |
|---|---|
| author | Muhammad Waqar (500788) |
| author2 | Mubbashir Ayub (7114544) |
| author2_role | author |
| author_facet | Muhammad Waqar (500788) Mubbashir Ayub (7114544) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Waqar (500788) Mubbashir Ayub (7114544) |
| dc.date.none.fl_str_mv | 2025-02-20T18:27:32Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0315533.g005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Agent_learning_of_ML_latest-small_dataset_across_various_parameters_/28453117 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified various online platforms reducing computation cost abundant digital data systems often fail given recommendation task changing user preferences art biclustering algorithms based recommendation systems low quality recommendations give personalized recommendations novel reinforcement learning appropriate biclustering algorithm recommendation algorithm recommender systems novel strategy learning process customer preferences proposed algorithm dynamic recommendations three datasets significant drawback results show movies domain list similarity innovative integration existing state existing literature efficient environment dynamically adjust dynamic nature diverse datasets core component computationally expensive adapt well |
| dc.title.none.fl_str_mv | Agent learning of ML latest-small dataset across various parameters. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Agent learning of ML latest-small dataset across various parameters.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_fdf145ce4d265c2d4b035f64e79992cc |
| identifier_str_mv | 10.1371/journal.pone.0315533.g005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28453117 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Agent learning of ML latest-small dataset across various parameters.Muhammad Waqar (500788)Mubbashir Ayub (7114544)BiotechnologyScience PolicySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedvarious online platformsreducing computation costabundant digital datasystems often failgiven recommendation taskchanging user preferencesart biclustering algorithmsbased recommendation systemslow quality recommendationsgive personalized recommendationsnovel reinforcement learningappropriate biclustering algorithmrecommendation algorithmrecommender systemsnovel strategylearning processcustomer preferencesproposed algorithmdynamic recommendationsthree datasetssignificant drawbackresults showmovies domainlist similarityinnovative integrationexisting stateexisting literatureefficient environmentdynamically adjustdynamic naturediverse datasetscore componentcomputationally expensiveadapt well<p>Agent learning of ML latest-small dataset across various parameters.</p>2025-02-20T18:27:32ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0315533.g005https://figshare.com/articles/figure/Agent_learning_of_ML_latest-small_dataset_across_various_parameters_/28453117CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284531172025-02-20T18:27:32Z |
| spellingShingle | Agent learning of ML latest-small dataset across various parameters. Muhammad Waqar (500788) Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified various online platforms reducing computation cost abundant digital data systems often fail given recommendation task changing user preferences art biclustering algorithms based recommendation systems low quality recommendations give personalized recommendations novel reinforcement learning appropriate biclustering algorithm recommendation algorithm recommender systems novel strategy learning process customer preferences proposed algorithm dynamic recommendations three datasets significant drawback results show movies domain list similarity innovative integration existing state existing literature efficient environment dynamically adjust dynamic nature diverse datasets core component computationally expensive adapt well |
| status_str | publishedVersion |
| title | Agent learning of ML latest-small dataset across various parameters. |
| title_full | Agent learning of ML latest-small dataset across various parameters. |
| title_fullStr | Agent learning of ML latest-small dataset across various parameters. |
| title_full_unstemmed | Agent learning of ML latest-small dataset across various parameters. |
| title_short | Agent learning of ML latest-small dataset across various parameters. |
| title_sort | Agent learning of ML latest-small dataset across various parameters. |
| topic | Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified various online platforms reducing computation cost abundant digital data systems often fail given recommendation task changing user preferences art biclustering algorithms based recommendation systems low quality recommendations give personalized recommendations novel reinforcement learning appropriate biclustering algorithm recommendation algorithm recommender systems novel strategy learning process customer preferences proposed algorithm dynamic recommendations three datasets significant drawback results show movies domain list similarity innovative integration existing state existing literature efficient environment dynamically adjust dynamic nature diverse datasets core component computationally expensive adapt well |