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研究生:陳志明
研究生(外文):Chen, Chih Ming
論文名稱:基於機器學習探討音樂多樣性推薦之研究
論文名稱(外文):Exploring Diverse Music Recommendation based on Machine Learning Approaches
指導教授:蔡銘峰蔡銘峰引用關係
指導教授(外文):Tsai, Ming Feng
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊科學學系
學門:工程學門
學類:電資工程學類
畢業學年度:102
語文別:英文
論文頁數:33
中文關鍵詞:推薦系統機器學習
外文關鍵詞:Recommendation SystemMachine Learning
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本論文提出了一種音樂推薦方法基於結合各種相似度資訊於分解機器(Factorization Machine)模型中。相似度的計算主要被廣泛使用於資訊檢索中,我們則是透過抽取內容與情境的相似度資訊方式,將此概念帶入至分解機器模型的架構裡,如此一來,不僅可以從大量的目標中擷取出具有相似特徵的群組,也能加速分解機器在學習中達到收斂結果的速度,這種透過結合不同數量的相似度特徵的方法還可以幫助使用者接觸到更多不同面向的物品。此外,加入大量的相似度資訊容易產生過多的計算量與雜訊,為了避免複雜度升高,我們採用了分群式的機器分解作為延伸的解決方法。在實驗裡,我們透過一個音樂資料的集合來展示我們提出的方法,此音樂資料集收集自一個線上的部落格網站,其中涵蓋了使用者聆聽音樂的記錄、使用者個人資料、社群資訊以及音樂資訊等相關內容。根據我們的實驗結果顯示,在結合各種相似度特徵的方法下,推薦的成效將會有顯著的提升,同時,調整不同數量的相似度資訊則可以單一化或者多樣化最後的推薦結果,最後,相對於傳統的協同過濾方法,在使用平均精確率平均(MeanAverage Precision)的標準之下,分群式的機器分解模型會也有顯著的成績提昇。
This paper proposes a music recommendation approach based on various similarity information via Factorization Machine (FM). We introduce the idea of similarity, which is widely studied in the filed of information retrieval, and incorporate multiple feature similarities into the FM framework, including content-based and context-based similarities. The similarity information not only captures the similar patterns from the referred objects, but enhances the convergence speed and accuracy of FM. By integrating different number of similarity features, the approach is even able to discover diverse objects that users never touched before. In addition, in order to avoid the high computational cost and noise within large similarity of features, we also adopt the grouping FM as the extended method to model the problem. In our experiments, a music-recommendation dataset is used to assess the performance of proposed approach. The dataset is collected from an online blogging website, which includes user listening history, user profiles, social information, and music information. Our experimental results show that, with the multiple feature similarities based on various types, the performance of music recommendation can be enhanced significantly. In the meantime the amount of similarity information can diversify the recommendations from a specific domain to a wide-ranging domain. Furthermore, via the grouping technique, the performance can be significant improved in terms of Mean Average Precision, compared to the traditional Collaborative Filtering approach.
1 Introduction 1
2 Related Work 5
2.1 ContextualRecommendationSystems ................... 5
2.2 MusicRecommendationSystems...................... 6
2.3 FactorizationMachines........................... 6
2.4 RecommendationDiversity......................... 7
3 Methodology 9
3.1 StandardFactorizationMachine ...................... 9
3.2 GroupingFactorizationMachine ...................... 9
3.3 SimilarityFramework............................ 10
3.4 ExtractedFeatures ............................. 12
3.4.1 Content-basedFeatures....................... 14
3.4.2 Context-basedFeatures....................... 15
4 Experimental Results 17
4.1 ExperimentalSettings............................ 17
4.1.1 Dataset ............................... 17
4.1.2 EvaluationMetrics ......................... 18
4.2 ContextualRecommendationSystem.................... 18
4.2.1 CF-basedRecommendations.................... 19
4.2.2 FMwithContent-basedFeatures.................. 20
4.2.3 FM with Content-based and Context-based Features . . . . . . . 20
4.3 SimilarityApproach............................. 21
4.3.1 UserSimilarityandItemSimilarity ................ 21
4.3.2 Content-basedfeaturesimilarity .................. 22
4.3.3 Context-basedfeaturesimilarity .................. 23
4.4 GroupingApproach............................. 24 9
4.4.1 TrainingLoss............................ 25
4.4.2 ModelComplexity ......................... 26
4.4.3 HybridRecommendations ..................... 27
4.5 RecommendationDiversity......................... 27
5 Conclusions 29
Bibliography 31
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