A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items
Content-Based recommender systems (CB) filter relevant items to users in overloaded search spaces using information about their preferences. However, classical CB scheme is mainly based on matching between items descriptions and user profile, without considering that context may influence user prefe...
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doaj-1358e018d6544595935eb50b61a01c732021-03-30T00:39:42ZengIEEEIEEE Access2169-35362019-01-01718266418267810.1109/ACCESS.2019.29578818924671A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer ItemsJorge Castro0https://orcid.org/0000-0003-4245-8813Raciel Yera Toledo1https://orcid.org/0000-0001-9759-261XAhmad A. Alzahrani2https://orcid.org/0000-0002-8081-6530Pedro J. Sanchez3https://orcid.org/0000-0002-4582-7760Manuel J. Barranco4https://orcid.org/0000-0002-2474-1909Luis Martinez5Computer Science Department, University of Jaén, Jaén, SpainKnowledge Management Center, University of Ciego de Ávila, Ciego de Ávila, CubaFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaComputer Science Department, University of Jaén, Jaén, SpainComputer Science Department, University of Jaén, Jaén, SpainComputer Science Department, University of Jaén, Jaén, SpainContent-Based recommender systems (CB) filter relevant items to users in overloaded search spaces using information about their preferences. However, classical CB scheme is mainly based on matching between items descriptions and user profile, without considering that context may influence user preferences. Therefore, it cannot achieve high accuracy on user preference prediction. This paper aims to handle context-awareness (CA) to improve quality of recommendation taking contextual information as the trend in current trend interest, in which a stream of status updates can be analyzed to model the context. It proposes a novel CA-CB approach that recommends question/answer items by considering context awareness based on topic detection within current trend interest. A case study and related experiments were developed in the big data framework Spark to show that the context integration benefits recommendation performance.https://ieeexplore.ieee.org/document/8924671/Content-based recommender systemcontext-awarenessuser profile contextualizationmap-reducebig data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jorge Castro Raciel Yera Toledo Ahmad A. Alzahrani Pedro J. Sanchez Manuel J. Barranco Luis Martinez |
spellingShingle |
Jorge Castro Raciel Yera Toledo Ahmad A. Alzahrani Pedro J. Sanchez Manuel J. Barranco Luis Martinez A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items IEEE Access Content-based recommender system context-awareness user profile contextualization map-reduce big data |
author_facet |
Jorge Castro Raciel Yera Toledo Ahmad A. Alzahrani Pedro J. Sanchez Manuel J. Barranco Luis Martinez |
author_sort |
Jorge Castro |
title |
A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items |
title_short |
A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items |
title_full |
A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items |
title_fullStr |
A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items |
title_full_unstemmed |
A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items |
title_sort |
big data semantic driven context aware recommendation method for question-answer items |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Content-Based recommender systems (CB) filter relevant items to users in overloaded search spaces using information about their preferences. However, classical CB scheme is mainly based on matching between items descriptions and user profile, without considering that context may influence user preferences. Therefore, it cannot achieve high accuracy on user preference prediction. This paper aims to handle context-awareness (CA) to improve quality of recommendation taking contextual information as the trend in current trend interest, in which a stream of status updates can be analyzed to model the context. It proposes a novel CA-CB approach that recommends question/answer items by considering context awareness based on topic detection within current trend interest. A case study and related experiments were developed in the big data framework Spark to show that the context integration benefits recommendation performance. |
topic |
Content-based recommender system context-awareness user profile contextualization map-reduce big data |
url |
https://ieeexplore.ieee.org/document/8924671/ |
work_keys_str_mv |
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