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研究生:張雅喬
研究生(外文):Ya-Chiao Zhang
論文名稱:利用使用者評論幫助協同過濾的評分預測
論文名稱(外文):The Use of User Reviews to Improve Rating Prediction of Collaborative Filtering
指導教授:鄭卜壬鄭卜壬引用關係
口試委員:曾新穆陳信希張嘉惠蔡銘峰
口試日期:2015-07-31
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:32
中文關鍵詞:協同過濾使用者評論推薦系統意涵路徑
外文關鍵詞:Collaborative filteringuser reviewsrecommender systemsmeta-path
相關次數:
  • 被引用被引用:0
  • 點閱點閱:110
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在推薦系統的研究中,協同過濾是很重要的方法,然而協同過濾存在著冷開始和資料稀疏等問題,所以我們想利用使用者評論來幫助協同過濾並提升評分預測的準確度。在本篇論文中,我們將使用者評論的情緒分數和協同過濾評分結合得到最終的預測評分,但因為評論本身所代表的意義和評分不盡相同,所以會造成評論情緒與評分不一致的狀況。我們提出將不同來源的權重變成一個以特徵為變數的函數,如此便可以讓模型學習如何根據特徵判斷使用者評論和協同過濾何者的重要度比較高。在特徵方面採用了協同特徵、使用者評論特徵與意涵路徑特徵,其中意涵路徑特徵可以為使用者和項目建立連結,這是一般協同過濾方法裡的使用者或項目相似度比較做不到的地方,而意涵路徑特徵在實驗中也發揮很大的效果,是表現最為突出的特徵。在實驗與討論的部分驗證了這個方法在評分預測上的表現、當來源品質不同時的影響、以及評論情緒與評分的不一致性,還有分析各種特徵對模型的重要程度。實驗結果顯示我們提出的來源權重函數能夠根據不同來源品質產生適當的權重分配,在預測評分的部份,我們的方法也贏過所有其他比較的方法。

Collaborative filtering (CF) is widely used in recommender systems. However, it suffers from cold-start problem and data sparsity problem. In this work, we take user reviews into consideration to help with CF performance. We combine sentiment of user reviews and predicted ratings of CF in rating prediction model through a source weighting function. The main idea is to use features to decide which source, the review or CF, is more reliable. There are three kinds of features in the model, collaborative features, user review features, and meta-path features. Unlike traditional user or item similarity, meta-path build links between users and items. The experiments show that meta-path has best performance among all features. We conduct experiments on rating prediction, the influence of different source quality, inconsistence of ratings and review sentiment, and feature importance analysis. The results show that our method outperforms compared methods on rating prediction and the source weighting function generates appropriate weights for sources given different kinds of source quality.

第一章  緒論 1
1.1 研究動機與挑戰 1
1.2 解決方法 2
1.3 章節架構 3
第二章  文獻探討 4
2.1 可幫助協同過濾的資源 4
2.2 使用者評論的應用 5
第三章  研究方法 8
3.1 方法架構 8
3.2 協同過濾方法 8
3.3 使用者評論情緒分析 9
3.4 權重函數 12
第四章  特徵 14
4.1 協同特徵 14
4.2 評論特徵 15
4.3 意涵路徑特徵 18
第五章  實驗與討論 22
5.1 實驗資料集 22
5.2 實驗設定 22
5.3 評分預測實驗與結果 23
5.4 特徵分析 24
5.5 來源品質實驗與結果 25
5.6 評論情緒與評分不一致性分析 28
5.7 評論的冷開始問題 29
第六章  結論與未來研究方向 30
6.1 結論 30
6.2 未來研究方向 30
參考文獻 31


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[2]Melville, Prem, Raymond J. Mooney, and Ramadass Nagarajan. "Content-boosted collaborative filtering for improved recommendations." AAAI/IAAI. 2002.
[3]Zhang, Mi, et al. "Addressing cold start in recommender systems: A semi-supervised co-training algorithm." Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 2014.
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[10]Ling, Guang, Michael R. Lyu, and Irwin King. "Ratings meet reviews, a combined approach to recommend." Proceedings of the 8th ACM Conference on Recommender systems. ACM, 2014.
[11]Pero, Štefan, and Tomáš Horváth. "Opinion-driven matrix factorization for rating prediction." User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2013. 1-13.
[12]Poirier, Damien, et al. "Towards text-based recommendations." Adaptivity, Personalization and Fusion of Heterogeneous Information. LE CENTRE DE HAUTES ETUDES INTERNATIONALES D''INFORMATIQUE DOCUMENTAIRE, 2010.
[13]Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 8 (2009): 30-37.
[14]Toutanova, Kristina, et al. "Feature-rich part-of-speech tagging with a cyclic dependency network." Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1. Association for Computational Linguistics, 2003.
[15]Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani. "SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining." LREC. Vol. 10. 2010.

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