推薦系統(tǒng)-從入門到精通為了方便大家從理論到實(shí)踐，從入門到精通，循序漸進(jìn)系統(tǒng)地理解和掌握推薦系統(tǒng)相關(guān)知識(shí)。特做了個(gè)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(biāo)明的經(jīng)典文獻(xiàn)以豐富各學(xué)科需求（為避免初學(xué)者疲于奔命，每個(gè)方向只推薦幾篇經(jīng)典文獻(xiàn)）。
1. 中文綜述(了解概念-入門篇)
a) 個(gè)性化推薦系統(tǒng)的研究進(jìn)展
b) 個(gè)性化推薦系統(tǒng)評(píng)價(jià)方法綜述
2. 英文綜述(了解概念-進(jìn)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(dòng)手能力(實(shí)踐算法-入門篇)
a) 2004ACMtois Item-based top-N recommendation algorithms(協(xié)同過濾)
b) 2007PRE Bipartite network projection and personal recommendation(網(wǎng)絡(luò)結(jié)構(gòu))
4. 動(dòng)手能力(實(shí)踐算法-進(jìn)階篇)
a) 2010PNAS-Solving the apparent diversity-accuracy dilemma of recommender systems (物質(zhì)擴(kuò)散和熱傳導(dǎo))
b) 2009NJP Accurate and diverse recommendations via eliminating redundant correlations (多步物質(zhì)擴(kuò)散)
c) 2008EPL Effect of initial configuration on network-based Recommendation (初始資源分配問題)
5. 推薦系統(tǒng)擴(kuò)展應(yīng)用(進(jìn)階篇)
a) 2009EPJB Predicting missing links via local information(相似性度量方法)
b) 2010theis-Evaluating Collaborative Filtering over time(基于時(shí)間效應(yīng)的博士論文)
c) 2009PA Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs (基于標(biāo)簽的三部分圖方法)
d) 2004LNCS Trust-aware collaborative filtering for recommender systems（基于信任機(jī)制）
e) 1997CA-Fab_content-based, collaborative recommendation(基于文本信息)
6. 推薦結(jié)果的解釋(進(jìn)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(tǒng)綜合篇(專著、大型綜述、博士論文)
a) 2005Ziegler-thesis-Towards Decentralized Recommender Systems
b) 2010Recommender Systems Handbook
