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  5. Mining Product Experiences from User Generated Reviews: A Recommender Systems Perspective
 
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Mining Product Experiences from User Generated Reviews: A Recommender Systems Perspective

Author(s)
Muhammad, Khalil  
Lawlor, Aonghus  
Rafter, Rachael  
Smyth, Barry  
Uri
http://hdl.handle.net/10197/6456
Date Issued
2014-09
Date Available
2015-04-01T12:28:33Z
Abstract
We have employed algorithms described in to mine opinions from TripAdvisor hotel reviews; we have experimented with different parameters to learn which provided more meaningful extractions. Secondly, we have considered opinion summarization and search similar to. We have implemented a retrieval strategy that accepts natural language queries based on opinions from reviews. Additionally, we have proposed various methods of summarizing opinions based on statistical metrics. Currently, we are experimenting with feature quality metrics. Our aim is to establish a relevance score that describes the usefulness of extracted opinions. We are also running recommendation experiments using different versions the extracted opinions.
Other Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Subjects

Recommender systems

Product descriptions

Web versions
http://www.iccbr.org/iccbr14/
Language
English
Status of Item
Peer reviewed
Conference Details
22nd International Conference on Case-Based Reasoning, Cork, Ireland, 29 September - 01 October 2014
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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insight_publication.pdf

Size

128.3 KB

Format

Adobe PDF

Checksum (MD5)

bc092afd89f732d3047b070bc3dcf9fe

Owning collection
Insight Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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