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DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張瑞益 | zh_TW |
dc.contributor.advisor | Ray-I Chang | en |
dc.contributor.author | 李嘉誠 | zh_TW |
dc.contributor.author | Jia-Cheng Li | en |
dc.date.accessioned | 2024-02-20T16:21:28Z | - |
dc.date.available | 2024-02-21 | - |
dc.date.copyright | 2024-02-20 | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-01-28 | - |
dc.identifier.citation | D. Bawden and L. Robinson, "Information overload: An overview," 2020.
F. Ricci, L. Rokach, and B. Shapira, "Recommender systems: introduction and challenges," Recommender systems handbook, pp. 1-34, 2015. Z. Lin, L. Feng, R. Yin, C. Xu, and C. K. Kwoh, "GLIMG: Global and local item graphs for top-N recommender systems," Information Sciences, vol. 580, pp. 1-14, 2021. "MovieLens 1M 資料集."https://grouplens.org/datasets/movielens/1m/ (accessed. "Yelp 2018 Challenge資料集."https://www.yelp.com/dataset_challenge (accessed. J. Karlgren, "An algebra for recommendations: Using reader data as a basis for measuring document proximity," ed: Department of Computer and Systems Sciences, Stockholm University, 1990. B. Smith and G. Linden, "Two decades of recommender systems at Amazon. com," Ieee internet computing, vol. 21, no. 3, pp. 12-18, 2017. C. A. Gomez-Uribe and N. Hunt, "The netflix recommender system: Algorithms, business value, and innovation," ACM Transactions on Management Information Systems (TMIS), vol. 6, no. 4, pp. 1-19, 2015. K. Jacobson, V. Murali, E. Newett, B. Whitman, and R. Yon, "Music personalization at Spotify," in Proceedings of the 10th ACM Conference on Recommender Systems, 2016, pp. 373-373. J. Liu, P. Dolan, and E. R. Pedersen, "Personalized news recommendation based on click behavior," in Proceedings of the 15th international conference on Intelligent user interfaces, 2010, pp. 31-40. "Amazon商品購買頁."https://www.amazon.com/-/zh/dp/B094Q89NKH/ref=sr_1_1?qid=1704054703&s=computers-intl-ship&sr=1-1&th=1 (accessed. 王国霞 and 刘贺平, "个性化推荐系统综述," 计算机工程与应用, vol. 7, 2012. Y. Bai, S. Jia, S. Wang, and B. Tan, "Customer loyalty improves the effectiveness of recommender systems based on complex network," Information, vol. 11, no. 3, p. 171, 2020. B. Perozzi, R. Al-Rfou, and S. Skiena, "Deepwalk: Online learning of social representations," in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp. 701-710. E. Palumbo, G. Rizzo, R. Troncy, E. Baralis, M. Osella, and E. Ferro, "Knowledge graph embeddings with node2vec for item recommendation," in The Semantic Web: ESWC 2018 Satellite Events: ESWC 2018 Satellite Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers 15, 2018: Springer, pp. 117-120. W. Hamilton, Z. Ying, and J. Leskovec, "Inductive representation learning on large graphs," Advances in neural information processing systems, vol. 30, 2017. R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, "Graph convolutional neural networks for web-scale recommender systems," in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 2018, pp. 974-983. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," in Proceedings of the 10th international conference on World Wide Web, 2001, pp. 285-295. P. Chanda, S. Yadav, and J. B. Pal, "Solving the Cold Start problem in Recommendation Systems-Case Study on MovieLens Dataset," 2022. "Part2 Movielens介绍."https://blog.csdn.net/weixin_41821131/article/details/123949686 (accessed. "Yelp Open Dataset."https://www.yelp.com/dataset (accessed. N. Asghar, "Yelp dataset challenge: Review rating prediction," arXiv preprint arXiv:1605.05362, 2016. "10个机器学习中常用的距离度量方法."https://zhuanlan.zhihu.com/p/581914013 (accessed. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91644 | - |
dc.description.abstract | 圖推薦模型非常適用於Top-N推薦系統,因為它們能夠捕獲實體之間的潛在關係。然而,大多數現有方法僅使用一個全域的物品圖(Item Graph),被所有使用者共用,並未考慮到不同使用者之間偏好的差異。先前有研究者設計出了一種新穎的圖推薦模型,名為GLIMG(Global and Local IteM Graphs),它可以同時捕獲全域和局部使用者的品味,實現更個性化的推薦。傳統GLIMG模型使用餘弦相似度來評估物品之間的相似性,由於不同相似度計算對於圖推薦系統之有所影響,因此在本研究中,我們在GLIMG模型原有的餘弦相似度計算基礎上探索是否存在更好的相似度計算方法來提升推薦系統的性能。
為保持實驗公平性,我們同樣使用Movielens-1M和Yelp2018資料集,並替換了GLIMG模型中的餘弦相似度計算方法,引入了三種不同的相似度計算方式,在模型中進行實驗,包括:歐氏距離、曼哈頓距離和調整後的餘弦相似度(Adjusted Cosine Similarity)。通過比較使用這些方法之後的各項推薦系統評估指標數值,我們發現,加上評分偏置項之後的調整後的餘弦相似度在Top-50的推薦任務上取得了最好的推薦效果。 | zh_TW |
dc.description.abstract | Graph-based recommendation models are highly suitable for Top-N recommendation systems as they can capture latent relationships between entities. However, most existing methods only utilize a global item graph shared by all users, neglecting differences in preferences among different users. A novel graph recommendation model named GLIMG (Global and Local IteM Graphs) has been previously designed to concurrently capture the tastes of both global and local users, achieving more personalized recommendations. The traditional GLIMG model uses cosine similarity to assess item similarity, a crucial metric for graph-based recommendation models. In this study, we explore whether there are better similarity calculation methods to enhance the performance of the recommendation system, building upon the original cosine similarity calculation in the GLIMG model.
To maintain experimental fairness, we employ the Movielens-1M and Yelp2018 datasets. We replace the cosine similarity calculation method in the GLIMG model with three different similarity calculation approaches – Euclidean distance, Manhattan distance, and adjusted cosine similarity. Through comparing various evaluation metrics of the recommendation system after incorporating these methods, we find that the adjusted cosine similarity, especially with the addition of rating bias, achieves the best recommendation performance in Top-50 recommendation tasks. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-02-20T16:21:28Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2024-02-20T16:21:28Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii 目次 iv 圖次 vi 表次 viii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 論文組織架構 2 第二章 文獻探討 3 2.1 推薦系統的介紹與應用 3 2.2 推薦系統的常用演算法和技術 5 2.3 圖推薦系統 6 2.4 推薦系統中常用的相似度計算方法 8 2.5 GLIMG模型介紹 9 第三章 研究方法 11 3.1 資料集選取及介紹 11 3.2 資料前處理 19 3.3 相似度計算方法分析 19 3.3.1 餘弦相似度 20 3.3.2 調整後的餘弦相似度 21 3.3.3 歐氏距離 22 3.3.4 曼哈頓距離 22 3.4 評估指標(Evaluation Metrics) 23 第四章 實驗結果與分析 25 4.1 實驗資料集 25 4.2 輸入和輸出 25 4.3 相減之後評分矩陣產生負值的解決方案 26 4.3.1 引入評分偏置項 28 4.3.2 引入非線性函數 30 4.4 比較不同評分偏置項對調整後的餘弦相似度產生之影響 31 4.5 不同負值消除方法對調整後的餘弦相似度產生之影響 33 4.6 比較各種相似度計算方式的推薦結果 34 4.7 比較不同資料稀疏程度對相似度計算的影響 37 4.7 對資料進行K-fold交叉驗證實驗 42 第五章 結論與展望 44 參考文獻 45 | - |
dc.language.iso | zh_TW | - |
dc.title | 不同相似度計算對於圖推薦系統之影響 | zh_TW |
dc.title | The Impact of Different Similarity Calculation Methods for Graph-based Recommendation System | en |
dc.type | Thesis | - |
dc.date.schoolyear | 112-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 張恆華;張信宏 | zh_TW |
dc.contributor.oralexamcommittee | Heng-Hua Chang;Shin-Hong Chang | en |
dc.subject.keyword | 推薦系統,GLIMG模型,相似度計算,Top-N推薦, | zh_TW |
dc.subject.keyword | Recommendation system,GLIMG model,Similarity calculation,Top-N recommendation, | en |
dc.relation.page | 47 | - |
dc.identifier.doi | 10.6342/NTU202400309 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2024-01-31 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 工程科學及海洋工程學系 | - |
顯示於系所單位: | 工程科學及海洋工程學系 |
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