Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/137295| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 蔡銘峰 | zh_TW |
| dc.contributor.advisor | Tsai, Ming-Feng | en_US |
| dc.contributor.author | 王均捷 | zh_TW |
| dc.contributor.author | Wang, Chun-Chieh | en_US |
| dc.creator | 王均捷 | zh_TW |
| dc.creator | Wang, Chun-Chieh | en_US |
| dc.date | 2021 | en_US |
| dc.date.accessioned | 2021-10-01T02:06:01Z | - |
| dc.date.available | 2021-10-01T02:06:01Z | - |
| dc.date.issued | 2021-10-01T02:06:01Z | - |
| dc.identifier | G0108753113 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/137295 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 資訊科學系 | zh_TW |
| dc.description | 108753113 | zh_TW |
| dc.description.abstract | 若我們有足夠多的歷史資料，就可以用很多不同的方法去建立一個聰明的推薦系統。但在某些情況下，比如一個新的社交媒體平台或電商平台上線時，我們沒有足夠的使用者物品互動資料來建構出好的推薦系統。其中一個強化跨領域推薦（cross-domain recommendation）的解決方案，是藉由將「來源領域（資訊含量較多之領域）」的資料加入「目標領域（資訊量相對較少的領域）」來提升資訊量，然後對「目標領域」進行推薦。\n\n本論文採用圖形學習表示演算法，結合改良並善用翻譯序列推薦模型（Translation-based Recommendation，TransRec）的推薦優勢，特化模型訓練時採樣方法、改變翻譯序列合併方法，並引入貝氏個人化推薦（Bayesian Personalized Ranking，BPR）中負採樣（negative sampling）的概念，訓練得到推薦系統任務導向之表示向量，藉此改善推薦結果。\n\n本研究旨在通過改良後的翻譯序列推薦模型「TransRecCross」來強化跨領域推薦效果。驗證本論文的新方法時，使用了 Amazon Review 系列資料集中的其中四個，並在論文最後比較了加入不同比例的來源領域資料後的推薦結果，以驗證本論文提出之方法的可靠程度。 | zh_TW |
| dc.description.abstract | There are plenty of ways to apply an intelligent domain-specific recommendation system if we have an enormous amount of data. Unfortunately, in some scenarios\nlike running a new social media platform or online store, we do not have enough user-item interactions data to build a sound recommendation system. One of the solutions is to apply cross-domain recommendation techniques: we can increase the amount of information by gathering the user-item interaction information from the "source domain" into the "target domain," and then implement the\nrecommendation system based on the "target domain."\n\nThis thesis adopts graph learning representation algorithm with extending the advantages of TransRec (Translation-based Recommendation): specializes the sampling method, changes the translating method, and then applies the negative sampling concept of BPR (Bayesian Personalized Ranking), then train and get the recommendation-oriented representation vectors to improve the recommendation system performance. This study aims to improve the cross-domain recommendation performance via a reformed translation-based recommendation model named "TransRecCross." In our experiments, comprehensive comparisons conducted on four datasets from Amazon Review have verified the effectiveness of the proposed TransRecCross. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1\n1.1 前言 1\n1.2 研究目的 2\n第二章 相關文獻探討 4\n2.1 圖形學習表示法 4\n2.2 個人化推薦系統 5\n2.3 跨領域推薦 7\n第三章 研究方法 9\n3.1 問題定義 9\n3.1.1 先備知識 9\n3.1.2 研究動機 12\n3.2 TransRecCross模型 13\n3.2.1 符號定義 13\n3.2.2 模型介紹 14\n3.2.3 採樣細節分析 17\n第四章 實驗結果與討論 19\n4.1 資料集 19\n4.2 比較基準模型 20\n4.3 實驗設定與驗證標準 22\n4.3.1 實驗設定 22\n4.3.2 驗證標準 22\n4.4 實驗結果 24\n4.4.1 跨領域推薦領域結果 24\n4.4.2 驗證跨領域強化能力 27\n第五章 結論 31\n參考文獻 32 | zh_TW |
| dc.format.extent | 1940359 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0108753113 | en_US |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 翻譯序列推薦 | zh_TW |
| dc.subject | 跨領域翻譯序列推薦 | zh_TW |
| dc.subject | 跨領域推薦 | zh_TW |
| dc.subject | 圖形學習 | zh_TW |
| dc.subject | 貝氏個人化推薦 | zh_TW |
| dc.subject | recommendation system | en_US |
| dc.subject | TransRec | en_US |
| dc.subject | TransRecCross | en_US |
| dc.subject | cross-domain recommendation | en_US |
| dc.subject | graph learning | en_US |
| dc.subject | BPR | en_US |
| dc.title | 基於翻譯序列推薦模型於跨領域推薦系統之強化方法 | zh_TW |
| dc.title | Employing Translation-based Recommendation for Improving Cross-domain Recommendation Performance | en_US |
| dc.type | thesis | en_US |
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Toprec: Domain-specific recommendation through community topic mining in social network. 05 2013.\n[28] Y. Zhang and X. Chen. Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval, 14(1):1–101, 2020.\n[29] C. Zhou, Y. Liu, X. Liu, Z. Liu, and J. Gao. Scalable graph embedding for asymmetric proximity. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI’17, page 2942–2948. AAAI Press, 2017.\n[30] F. Zhu, Y. Wang, C. Chen, J. Zhou, L. Li, and G. Liu. Cross-domain recommendation: Challenges, progress, and prospects, 2021. | zh_TW |
| dc.identifier.doi | 10.6814/NCCU202101565 | en_US |
| item.fulltext | With Fulltext | - |
| item.openairetype | thesis | - |
| item.cerifentitytype | Publications | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| item.grantfulltext | open | - |
| Appears in Collections: | 學位論文 | |
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| 311301.pdf | 1.89 MB | Adobe PDF2 | View/Open |
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