Advanced Reading of the Recommendation System (from entry to entry)

Recommendation System-from entry-level to proficient

In order to facilitate everyone from theory to practice, from entry to proficiency, gradually and systematically understand and master the knowledge of the recommendation system. I made a reading list. You can read this table, and you are welcome to give comments and point out the unspecified classical literature to enrich the needs of various disciplines (to prevent beginners from getting tired, only a few classical documents are recommended in each direction ).
1. Chinese summary (understanding concepts-Getting Started)
A) Research Progress of Personalized Recommendation Systems
B) summary of Personalized Recommendation System Evaluation Methods
2. Overview in English (understanding concepts-advanced)
A) 2004acmtois-evaluating collaborative filtering recommender systems
B) 2004 acmtois-Introduction to recommender systems-algorithms and evaluation
C) 2005 ieeetkde toward the next generation of recommender systems-a survey of the state-of-the-art and possible extensions
3. Hands-on capabilities (practical algorithms-Quick Start)
A) 2004 acmtois item-based top-N recommendation algorithms (collaborative filtering)
B) 2007pre bipartite network projection and personal recommendation (network structure)
4. Hands-on capabilities (practical algorithms-advanced)
A) 2010pnas-solving the apparent diversity-accuracy dilemma of recommender systems (material diffusion and Heat Conduction)
B) 2009njp accurate and diverse recommendations via eliminating redundant correlations (multi-step material diffusion)
C) 2008epl effect of initial configuration on network-based recommendation (initial resource allocation problem)
5. Recommendation System extended applications (advanced)
A) 2009 epjb predicting missing links via local information (similarity measurement method)
B) 2010theis-evaluating collaborative filtering over time (time-based doctoral thesis)
C) 2009 PA Personalized Recommendation via integrated diffusion on user-item-tag tripartite graphs (tag-based three-part Graph Method)
D) 2004 lncs trust-aware collaborative filtering for recommender systems (based on the trust mechanism)
E) 1997ca-fab_content-based, collaborative recommendation (based on text information)
6. Explanation of Recommendation results (Advanced article)
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. Comprehensive recommendation system (monograph, large-scale review, doctoral thesis)
A) 2005ziegler-thesis-towards decentralized recommender systems
B) 2010 recommender systems Handbook


Reference address:Http://blog.sciencenet.cn/blog-210641-508634.html