Incorporating Pairwise Learning into Latent Dirichlet Allocation for Effective Item Recommendations
碩士 === 國立臺灣大學 === 資訊管理學研究所 === 106 === Internet e-services now provide so many items that effective recommendation systems are crucial to users to search for desire items. In this paper, we present a new recommendation method which is based on theoretical graphical models. We incorporate the concept...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | en_US |
Published: |
2018
|
Online Access: | http://ndltd.ncl.edu.tw/handle/3n5h8w |
id |
ndltd-TW-106NTU05396055 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-106NTU053960552019-07-25T04:46:49Z http://ndltd.ncl.edu.tw/handle/3n5h8w Incorporating Pairwise Learning into Latent Dirichlet Allocation for Effective Item Recommendations 結合成對學習與隱含狄利克雷分布之項目推薦 Ming-Chen Wu 吳洺甄 碩士 國立臺灣大學 資訊管理學研究所 106 Internet e-services now provide so many items that effective recommendation systems are crucial to users to search for desire items. In this paper, we present a new recommendation method which is based on theoretical graphical models. We incorporate the concept of pairwise learning into the latent Dirichlet allocation model to discover user preferences which differentiate users’ precedence on items. A voting mechanism applied to the learned user preferences is devised so that favorite items are suggested to the users. Preliminary experiments based on a real-world dataset demonstrate that the discovered user preferences are effective in item recommendations. Also, incorporating pairwise learning successfully enhances the LDA based recommendation method in terms of the recommendation precision. Chien-Chin Chen 陳建錦 2018 學位論文 ; thesis 26 en_US |
collection |
NDLTD |
language |
en_US |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立臺灣大學 === 資訊管理學研究所 === 106 === Internet e-services now provide so many items that effective recommendation systems are crucial to users to search for desire items. In this paper, we present a new recommendation method which is based on theoretical graphical models. We incorporate the concept of pairwise learning into the latent Dirichlet allocation model to discover user preferences which differentiate users’ precedence on items. A voting mechanism applied to the learned user preferences is devised so that favorite items are suggested to the users. Preliminary experiments based on a real-world dataset demonstrate that the discovered user preferences are effective in item recommendations. Also, incorporating pairwise learning successfully enhances the LDA based recommendation method in terms of the recommendation precision.
|
author2 |
Chien-Chin Chen |
author_facet |
Chien-Chin Chen Ming-Chen Wu 吳洺甄 |
author |
Ming-Chen Wu 吳洺甄 |
spellingShingle |
Ming-Chen Wu 吳洺甄 Incorporating Pairwise Learning into Latent Dirichlet Allocation for Effective Item Recommendations |
author_sort |
Ming-Chen Wu |
title |
Incorporating Pairwise Learning into Latent Dirichlet Allocation for Effective Item Recommendations |
title_short |
Incorporating Pairwise Learning into Latent Dirichlet Allocation for Effective Item Recommendations |
title_full |
Incorporating Pairwise Learning into Latent Dirichlet Allocation for Effective Item Recommendations |
title_fullStr |
Incorporating Pairwise Learning into Latent Dirichlet Allocation for Effective Item Recommendations |
title_full_unstemmed |
Incorporating Pairwise Learning into Latent Dirichlet Allocation for Effective Item Recommendations |
title_sort |
incorporating pairwise learning into latent dirichlet allocation for effective item recommendations |
publishDate |
2018 |
url |
http://ndltd.ncl.edu.tw/handle/3n5h8w |
work_keys_str_mv |
AT mingchenwu incorporatingpairwiselearningintolatentdirichletallocationforeffectiveitemrecommendations AT wúmíngzhēn incorporatingpairwiselearningintolatentdirichletallocationforeffectiveitemrecommendations AT mingchenwu jiéhéchéngduìxuéxíyǔyǐnhándílìkèléifēnbùzhīxiàngmùtuījiàn AT wúmíngzhēn jiéhéchéngduìxuéxíyǔyǐnhándílìkèléifēnbùzhīxiàngmùtuījiàn |
_version_ |
1719230364886499328 |