A Context Based Recommender System through Collaborative Filtering and Word Embedding Techniques
نویسنده:
, , , , , , ,سال
: 2016
چکیده: This report presents a description of the context-based recommender system that
was developed by the FUM-IR team from the Ferdowsi University of Mashhad for
the Contextual Suggestion track of TREC 2016. This will also include the description
of the different runs were submitted to this track. In developing our system, we
followed two main approaches for finding suitable attractions for a given user: a
content-based approach and a category-based approach.
was developed by the FUM-IR team from the Ferdowsi University of Mashhad for
the Contextual Suggestion track of TREC 2016. This will also include the description
of the different runs were submitted to this track. In developing our system, we
followed two main approaches for finding suitable attractions for a given user: a
content-based approach and a category-based approach.
کلیدواژه(گان): Recommender systems,context based,Word embedding
کالکشن
:
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آمار بازدید
A Context Based Recommender System through Collaborative Filtering and Word Embedding Techniques
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| contributor author | مهسا خراسانی | en |
| contributor author | سیدحمید سجادی | en |
| contributor author | فائزه رمضانی | en |
| contributor author | فائزه انسان | en |
| contributor author | mahsa khorasani | fa |
| contributor author | Hamid Sajjadi | fa |
| contributor author | faeze ramezani | fa |
| contributor author | F Ensan | fa |
| date accessioned | 2020-06-06T14:25:04Z | |
| date available | 2020-06-06T14:25:04Z | |
| date copyright | 11/10/2016 | |
| date issued | 2016 | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/3395795 | |
| description abstract | This report presents a description of the context-based recommender system that was developed by the FUM-IR team from the Ferdowsi University of Mashhad for the Contextual Suggestion track of TREC 2016. This will also include the description of the different runs were submitted to this track. In developing our system, we followed two main approaches for finding suitable attractions for a given user: a content-based approach and a category-based approach. | en |
| language | English | |
| title | A Context Based Recommender System through Collaborative Filtering and Word Embedding Techniques | en |
| type | Conference Paper | |
| contenttype | External Fulltext | |
| subject keywords | Recommender systems | en |
| subject keywords | context based | en |
| subject keywords | Word embedding | en |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1062204.html | |
| conference title | TREC 2016 | en |
| conference location | واشنگتن | fa |
| identifier articleid | 1062204 |