A Triple Wing Harmonium Model for Movie Recommendation
A new triple wing harmonium (TWH) model that integrates text metadata into a low-dimensional semantic space is proposed for the application of content-based movie recommendation. The text metadata considered here include movie synopsis, actor list, and user comments. We develop a new TWH model proje...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2016-02, Vol.12 (1), p.231-239 |
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| container_title | IEEE transactions on industrial informatics |
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| creator | Zhang, Haijun Ji, Yuzhu Li, Jingxuan Ye, Yunming |
| description | A new triple wing harmonium (TWH) model that integrates text metadata into a low-dimensional semantic space is proposed for the application of content-based movie recommendation. The text metadata considered here include movie synopsis, actor list, and user comments. We develop a new TWH model projecting these multiple textual features into low-dimensional latent topics with different probability distribution assumptions. A contrastive divergence (CD) algorithm is used for efficient learning and inference. Experimental results suggest that the proposed method performs better than the state-of-the-art algorithms for movie recommendation. |
| doi_str_mv | 10.1109/TII.2015.2475218 |
| format | Article |
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The text metadata considered here include movie synopsis, actor list, and user comments. We develop a new TWH model projecting these multiple textual features into low-dimensional latent topics with different probability distribution assumptions. A contrastive divergence (CD) algorithm is used for efficient learning and inference. 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The text metadata considered here include movie synopsis, actor list, and user comments. We develop a new TWH model projecting these multiple textual features into low-dimensional latent topics with different probability distribution assumptions. A contrastive divergence (CD) algorithm is used for efficient learning and inference. Experimental results suggest that the proposed method performs better than the state-of-the-art algorithms for movie recommendation.</description><subject>Adaptation models</subject><subject>Data models</subject><subject>Feature extraction</subject><subject>Harmonium model</subject><subject>Inference algorithms</subject><subject>Motion pictures</subject><subject>movie recommendation</subject><subject>multiple features</subject><subject>Neurons</subject><subject>Semantics</subject><subject>text metadata</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWKt3wcuC560zyebrWIraQkWQisewm2Qlpbup2Vbw3zelxdO8h-edGR5C7hEmiKCfVovFhALyCa0kp6guyAh1hSUAh8ucOceSUWDX5GYY1gBMAtMjIqbFKoXtxhdfof8u5nXqYh_2XfEWnd8UbUw5_QZffHgbu873rt6F2N-Sq7beDP7uPMfk8-V5NZuXy_fXxWy6LC3VuCu5U5aio02rhZPaNsLXXAmWP_NSqMZZ9FZzUVWuwZaxJhP5U6BOu8ZRxcbk8bR3m-LP3g87s4771OeTBqWUFQdUPFNwomyKw5B8a7YpdHX6MwjmaMdkO-Zox5zt5MrDqRK89_-4pAyoUOwAq_5e9g</recordid><startdate>20160201</startdate><enddate>20160201</enddate><creator>Zhang, Haijun</creator><creator>Ji, Yuzhu</creator><creator>Li, Jingxuan</creator><creator>Ye, Yunming</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE transactions on industrial informatics, 2016-02, Vol.12 (1), p.231-239 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptation models Data models Feature extraction Harmonium model Inference algorithms Motion pictures movie recommendation multiple features Neurons Semantics text metadata |
| title | A Triple Wing Harmonium Model for Movie Recommendation |
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