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        <title>Distributed Representations for Building Profiles of Users and Items from Text Reviews</title>
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        <namePart type="given">Wenliang</namePart>
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    <name type="personal">
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
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    <abstract>In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems. Traditional recommendation algorithms, e.g. collaborative filtering and matrix completion, are not designed to exploit the key information hidden in the text comments, while existing opinion mining methods do not provide direct support to recommendation systems with useful features on users and items. Our approach attempts to construct vectors to represent profiles of users and items under a unified framework to maximize word appearance likelihood. Then, the vector representations are used for a recommendation task in which we predict scores on unobserved user-item pairs without given texts. The recommendation-aware distributed representation approach is fully supported by effective and efficient learning algorithms over massive text archive. Our empirical evaluations on real datasets show that our system outperforms the state-of-the-art baseline systems.</abstract>
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        <url>https://aclanthology.org/C16-1202</url>
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%0 Conference Proceedings
%T Distributed Representations for Building Profiles of Users and Items from Text Reviews
%A Chen, Wenliang
%A Zhang, Zhenjie
%A Li, Zhenghua
%A Zhang, Min
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F chen-etal-2016-distributed
%X In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems. Traditional recommendation algorithms, e.g. collaborative filtering and matrix completion, are not designed to exploit the key information hidden in the text comments, while existing opinion mining methods do not provide direct support to recommendation systems with useful features on users and items. Our approach attempts to construct vectors to represent profiles of users and items under a unified framework to maximize word appearance likelihood. Then, the vector representations are used for a recommendation task in which we predict scores on unobserved user-item pairs without given texts. The recommendation-aware distributed representation approach is fully supported by effective and efficient learning algorithms over massive text archive. Our empirical evaluations on real datasets show that our system outperforms the state-of-the-art baseline systems.
%U https://aclanthology.org/C16-1202
%P 2143-2153

Markdown (Informal)

[Distributed Representations for Building Profiles of Users and Items from Text Reviews](https://aclanthology.org/C16-1202) (Chen et al., COLING 2016)

• Distributed Representations for Building Profiles of Users and Items from Text Reviews (Chen et al., COLING 2016)

ACL

• Wenliang Chen, Zhenjie Zhang, Zhenghua Li, and Min Zhang. 2016. Distributed Representations for Building Profiles of Users and Items from Text Reviews. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2143–2153, Osaka, Japan. The COLING 2016 Organizing Committee.