推薦系統的循序進階讀物（從入門到精通）

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推薦系統-從入門到精通

為了方便大家從理論到實踐，從入門到精通，循序漸進系統地理解和掌握推薦系統相關知識。特做了個讀物清單。大家可以按此表閱讀，也歡迎提出意見和指出未標明的經典文獻以豐富各學科需求（為避免初學者疲於奔命，每個方向只推薦幾篇經典文獻）。
1. 中文綜述(瞭解概念-入門篇)
a) 個人化推薦系統的研究進展
b) 個人化推薦系統評價方法綜述
2. 英文綜述(瞭解概念-進階篇)
a) 2004ACMTois-Evaluating collaborative filtering recommender systems
b) 2004ACMTois -Introduction to Recommender Systems - Algorithms and evaluation
c) 2005IEEEtkde Toward the next generation of recommender systems - A survey of the state-of-the-art and possible extensions
3. 動手能力(實踐演算法-入門篇)
a) 2004ACMtois Item-based top-N recommendation algorithms(協同過濾)
b) 2007PRE Bipartite network projection and personal recommendation(網路結構)
4. 動手能力(實踐演算法-進階篇)
a) 2010PNAS-Solving the apparent diversity-accuracy dilemma of recommender systems (物質擴散和熱傳導)
b) 2009NJP Accurate and diverse recommendations via eliminating redundant correlations (多步物質擴散)
c) 2008EPL Effect of initial configuration on network-based Recommendation (初始資源分派問題)
5. 推薦系統擴充應用(進階篇)
a) 2009EPJB Predicting missing links via local information(相似性度量方法)
b) 2010theis-Evaluating Collaborative Filtering over time(基於時間效應的博士論文)
c) 2009PA Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs (基於標籤的三部分圖方法)
d) 2004LNCS Trust-aware collaborative filtering for recommender systems（基於信任機制）
e) 1997CA-Fab_content-based, collaborative recommendation(基於文本資訊)
6. 推薦結果的解釋(進階篇)
a) 2000CSCW-Explaining Collaborative Filtering Recommendations
b) 2011PRE-Information filtering via biased heat conduction
c) 2011PRE- Information filtering via preferential diffusion
d) 2010EPL Link Prediction in weighted networks - The role of weak ties
e) 2010EPL-Solving the cold-start problem in recommender systems with social tags
7. 推薦系統綜合篇(專著、大型綜述、博士論文)
a) 2005Ziegler-thesis-Towards Decentralized Recommender Systems
b) 2010Recommender Systems Handbook


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