標題: 以協同主題模式與跨領域分析為基礎之線上電影推薦方法
Online Movie Recommendation Approach based on Collaborative Topic Modeling and Cross-Domain Analysis
作者: 簡巧婷
劉敦仁
Jian, Ciao-Ting
Liu, Duen-Ren
資訊管理研究所
關鍵字: 電影推薦;矩陣分解;隱含主題模式;協同主題模式;跨領域;多樣性;Movie Recommendation;Latent Topic Model;Collaborative Topic Modeling;Association Rules;Cross-domain;Diversity
公開日期: 2017
摘要: 隨著互聯網的蓬勃發展和新型態的網路資訊與電子商務平台之興起,越來越多使用者透過網路資訊平台來獲取特定主題如生活時尚新聞等資訊。從網路資訊平台可以分析使用者的瀏覽行為與喜好成功推薦新資訊,以吸引更多使用者並提升資訊平台之流量,是目前網路平台重要發展趨勢。然而,資訊平台之多角化發展促使資訊平台提供之相關資訊量爆炸且愈趨複雜,對於用戶來說,要尋找自己感興趣的資訊已經成為一件困難的事情。因此,藉由良好的線上推薦機制來推薦使用者有興趣之相關資訊,並提高使用者的點擊率,是目前電子商務平台IT技術中不可或缺的一環。 本研究整合跨領域資訊來源及喜好多樣性分析,研發一個新的線上電影推薦機制,並進行線上推薦評估與比較。然而,隨著網站資訊平台規模的擴大,使用者人數和項目數據急遽增加,導致使用者的瀏覽資料量的極端稀疏性(Data Sparsity),在如此的情況下,傳統推薦方法的推薦成效較不佳。因此,本研究將以關聯規則(Association Rules)及隱含主題模式(LDA)探勘為基礎,進行跨領域分析,並結合協同主題模式(CTM),來預測使用者歷史與線上電影喜好,並分析其喜好多樣性或單一性程度,研發新的線上電影推薦方法,以解決資料稀疏性問題。實驗結果顯示,本研究所提出的方法能有效改善cold-start問題,並且提升使用者的電影點擊率。
With the rapid development of the Internet and the rise of new types of news websites with e-commerce portals, more and more users obtain specific topics online information. Successfully information recommendation to users by analyzing users’ browsing behaviors and preferences in the web-based platform can attract more users and enhance the information flow of platform, which is an important trend of the current online worlds. However, information provided by news websites is exploding and becoming more complicated. Therefore, it is an indispensable part of IT technology for e-commerce platforms to deploy appropriate online recommendation methods to improve the users’ click-through rates. In this research, we conduct cross-domain and diversity analysis of user preferences to develop novel online movie recommendation methods and evaluated online recommendation results. Specifically, association rule mining is conducted on user browsing news and moves to find the latent associations between news and movies. A novel online recommendation approach is proposed to predict user preferences for movies based on Latent Dirichlet Allocation, Collaborative Topic Modeling and the diversity of recommendations. The experimental results show that the proposed approach can improve the cold-start problem and enhance the click-through rate of movies.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070453418
http://hdl.handle.net/11536/141876
顯示於類別:畢業論文