Profile Inference from Heterogeneous Data - Fundamentals and New Trends

One of the essential steps in most business is to understand customers’ preferences. In a data-centric era, profile inference is more and more relaying on mining increasingly accumulated and usually anonymous (protected) data. Personalized profile (preferences) of an anonymous user can even be recovered by some data technologies. The aim of the paper is to review some commonly used information retrieval techniques in recommendation systems and introduce new trends in heterogeneous information network based and knowledge graph based approaches. Then business developers can get some insights on what kind of data to collect as well as how to store and manage them so that better decisions can be made after analyzing the data and extracting the needed information.

[1]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  Georgia Koutrika,et al.  FlexRecs: expressing and combining flexible recommendations , 2009, SIGMOD Conference.

[3]  Yiming Wang,et al.  Learning with Linear Mixed Model for Group Recommendation Systems , 2019, ICMLC '19.

[4]  Greg Linden,et al.  Two Decades of Recommender Systems at Amazon.com , 2017, IEEE Internet Computing.

[5]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[6]  Sheikh Muhammad Sarwar,et al.  A New User Similarity Computation Method for Collaborative Filtering Using Artificial Neural Network , 2014, EANN.

[7]  Zhendong Mao,et al.  Knowledge Graph Embedding: A Survey of Approaches and Applications , 2017, IEEE Transactions on Knowledge and Data Engineering.

[8]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[9]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[10]  Palash Goyal,et al.  Graph Embedding Techniques, Applications, and Performance: A Survey , 2017, Knowl. Based Syst..

[11]  Shengxin Zhu,et al.  Fast calculation of restricted maximum likelihood methods for unstructured high-throughput data , 2017, 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(.

[12]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[13]  Xiaoqin Zeng,et al.  Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph , 2018, Mathematical Problems in Engineering.

[14]  Fernando Ortega,et al.  Improving collaborative filtering recommender system results and performance using genetic algorithms , 2011, Knowl. Based Syst..

[15]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[16]  Yizhou Sun,et al.  Personalized entity recommendation: a heterogeneous information network approach , 2014, WSDM.

[17]  Xiaowen Xu,et al.  Information Splitting for Big Data Analytics , 2016, 2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC).

[18]  Jesús Bobadilla,et al.  A new collaborative filtering metric that improves the behavior of recommender systems , 2010, Knowl. Based Syst..

[19]  Yongfeng Huang,et al.  SigRA: A New Similarity Computation Method in Recommendation System , 2017, 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC).

[20]  F. Serradilla,et al.  Choice of metrics used in collaborative filtering and their impact on recommender systems , 2008, 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies.

[21]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[22]  John Riedl,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[23]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[24]  Philip S. Yu,et al.  PathSim , 2011, Proc. VLDB Endow..

[25]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.