Challenges and Solutions in Recommender Systems

Recommender Systems play a huge part in most of our lives today. A large portion of today’s digital customers rely on such programs to shape their usage of online markets. The main objective of such systems is to build relationships between the products and its users and to help them make the best decisions depending on their needs. There are 4 main types of Recommender Systems that follow different methods in order to satisfy user preferences by filtering through data in an efficient manner.

[1]  Li Chen,et al.  Evaluating recommender systems from the user’s perspective: survey of the state of the art , 2012, User Modeling and User-Adapted Interaction.

[2]  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.

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

[4]  David Sánchez,et al.  Turist@: Agent-based personalised recommendation of tourist activities , 2012, Expert Syst. Appl..

[5]  Sarika Jain,et al.  Trends, problems and solutions of recommender system , 2015, International Conference on Computing, Communication & Automation.

[6]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[7]  Gediminas Adomavicius,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005, TOIS.

[8]  Hao Jiang,et al.  Personalized online document, image and video recommendation via commodity eye-tracking , 2008, RecSys '08.

[9]  Abolghasem Sadeghi-Niaraki,et al.  Ontology based personalized route planning system using a multi-criteria decision making approach , 2009, Expert Syst. Appl..

[10]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[11]  Zafar Ali,et al.  Recommender Systems: Issues, Challenges, and Research Opportunities , 2016 .

[12]  Adam Prügel-Bennett,et al.  Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems , 2014, Expert Syst. Appl..

[13]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.

[14]  Mohamed Helmy Khafagy,et al.  Recommender Systems Challenges and Solutions Survey , 2019, 2019 International Conference on Innovative Trends in Computer Engineering (ITCE).

[15]  Dennis McLeod,et al.  Yoda: An Accurate and Scalable Web-Based Recommendation System , 2001, CoopIS.