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| Title | ICE: Item Concept Embedding via Textual Information |
| Creator | 蔡銘峰 Wang, Chuan-Ju Wang, Ting-Hsiang Yang, Hsiu-Wei Chang, Bo-Sin Tsai, Ming-Feng |
| Contributor | 資訊科學系 |
| Key Words | concept embedding; conceptual retrieval; information network; textual information |
| Date | 2017-08 |
| Date Issued | 29-Aug-2017 13:24:20 (UTC+8) |
| Summary | This paper proposes an item concept embedding (ICE) framework to model item concepts via textual information. Specifically, in the proposed framework there are two stages: graph construction and embedding learning. In the first stage, we propose a generalized network construction method to build a network involving heterogeneous nodes and a mixture of both homogeneous and heterogeneous relations. The second stage leverages the concept of neighborhood proximity to learn the embeddings of both items and words. With the proposed carefully designed ICE networks, the resulting embedding facilitates both homogeneous and heterogeneous retrieval, including item-to-item and word-to-item retrieval. Moreover, as a distributed embedding approach, the proposed ICE approach not only generates related retrieval results but also delivers more diverse results than traditional keyword-matching-based approaches. As our experiments on two real-world datasets show, ICE encodes useful textual information and thus outperforms traditional methods in various item classification and retrieval tasks. |
| Relation | Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pages 85-94 |
| Type | conference |
| DOI | http://dx.doi.org/10.1145/3077136.3080807 |
| dc.contributor | 資訊科學系 | zh_TW |
| dc.creator (作者) | 蔡銘峰 | zh_TW |
| dc.creator (作者) | Wang, Chuan-Ju | en_US |
| dc.creator (作者) | Wang, Ting-Hsiang | en_US |
| dc.creator (作者) | Yang, Hsiu-Wei | en_US |
| dc.creator (作者) | Chang, Bo-Sin | en_US |
| dc.creator (作者) | Tsai, Ming-Feng | en_US |
| dc.date (日期) | 2017-08 | en_US |
| dc.date.accessioned | 29-Aug-2017 13:24:20 (UTC+8) | - |
| dc.date.available | 29-Aug-2017 13:24:20 (UTC+8) | - |
| dc.date.issued (上傳時間) | 29-Aug-2017 13:24:20 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/112296 | - |
| dc.description.abstract (摘要) | This paper proposes an item concept embedding (ICE) framework to model item concepts via textual information. Specifically, in the proposed framework there are two stages: graph construction and embedding learning. In the first stage, we propose a generalized network construction method to build a network involving heterogeneous nodes and a mixture of both homogeneous and heterogeneous relations. The second stage leverages the concept of neighborhood proximity to learn the embeddings of both items and words. With the proposed carefully designed ICE networks, the resulting embedding facilitates both homogeneous and heterogeneous retrieval, including item-to-item and word-to-item retrieval. Moreover, as a distributed embedding approach, the proposed ICE approach not only generates related retrieval results but also delivers more diverse results than traditional keyword-matching-based approaches. As our experiments on two real-world datasets show, ICE encodes useful textual information and thus outperforms traditional methods in various item classification and retrieval tasks. | en_US |
| dc.format.extent | 1574416 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pages 85-94 | en_US |
| dc.subject (關鍵詞) | concept embedding; conceptual retrieval; information network; textual information | en_US |
| dc.title (題名) | ICE: Item Concept Embedding via Textual Information | en_US |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1145/3077136.3080807 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1145/3077136.3080807 |