Title : ( A Context Based Recommender System through Collaborative Filtering and Word Embedding Techniques )
Authors: mahsa khorasani , Hamid Sajjadi , faeze ramezani , F Ensan ,
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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.
Keywords
, Recommender systems, context based, Word embedding
@inproceedings{paperid:1062204,
author = {Khorasani, Mahsa and Sajjadi, Hamid and Ramezani, Faeze and Ensan, F},
title = {A Context Based Recommender System through Collaborative Filtering and Word Embedding Techniques},
booktitle = {TREC 2016},
year = {2016},
location = {واشنگتن, USA},
keywords = {Recommender systems; context based; Word embedding},
}
%0 Conference Proceedings
%T A Context Based Recommender System through Collaborative Filtering and Word Embedding Techniques
%A Khorasani, Mahsa
%A Sajjadi, Hamid
%A Ramezani, Faeze
%A Ensan, F
%J TREC 2016
%D 2016
