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Libros:

Lectura: «Recommender Systems Handbook» ―Francesco Ricci et al.

Fuente: ricci-et-al.recommender-systems-handbook_2ed.bk.pdficc

Publicaciones

Lectura: «Recommender systems survey» ―Bobadilla, Ortega, Hernando & Gutiérrez

Fuente: <bobadilla-et-al.recommender-systems-survey.wp.pdf>

“Recommender Systems (RSs) collect information on the preferences of its users for a set of items”

“The information can be acquired explicitly (typically by collecting users’ ratings) or implicitly [134,60,164] (typically by monitoring users’ behavior, such as songs heard, applications downloaded, web sites visited and books read)”

“RS may use demographic features of users (like age, nationality, gender).”

Social information, like followers, followed, twits, and posts, is commonly used in Web 2.0.

There is a growing tend towards the use of information from Internet of things (e.g., GPS locations, RFID, real-time health signals).

RS make use of different sources of information for providing users with predictions and recommendations of items.

They try to balance factors like accuracy, novelty, dispersity and stability in the recommendations.

Collaborative Filtering (CF) methods play an important role in the recommendation, although they are often used along with other filterning techniques like content-based, knowledge-based or social ones.

CF is based on the way in which humans have made decisions throughout history: besides on our own experiences, we also base our decisions on the experiences and knowledge that reach each of us from a relatively large group of acquaintances.

[171] OK: D.H. Park, H.K. Kim, I.Y. Choi, J.K. Kim, A literature review and classification of recommender Systems research, Expert Systems with Applications 39 (2012) 10059–10072.

Z. Huang, D. Zeng, H. Chen, A comparison of collaborative filtering recommendation algorithms for e-commerce, IEEE Intelligent Systems 22 (5) (2007) 68–78.

J.J. Castro-Sanchez, R. Miguel, D. Vallejo, L.M. López-López, A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals, Expert Systems with Applications 38 (3) (2011) 2441–2454.

E. Costa-Montenegro, A.B. Barragáns-Martı ´ nez, M. Rey-López, Which App? A recommender system of applications in markets: implementation of the service for monitoring users’ interaction, Expert Systems with Applications 39 (10) (2012) 9367–9375.

S.K. Lee, Y.H. Cho, S.H. Kim, Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations, Information Sciences 180 (11) (2010) 2142–2155.

K. Choi, D. Yoo, G. Kim, Y. Suh, A hybrid online-product recommendation system: combining implicit rating-based collaborative filtering and sequential pattern analysis. Electronic Commerce Research and Applications, in press, doi: 10.1016/j.elerap.2012.02.004.

E.R. Núñez-Valdéz, J.M. Cueva-Lovelle, O. Sanjuán-Martínez, V. García-Díaz, P. Ordoñez, C.E. Montenegro-Marín, Implicit feedback techniques on recommender systems applied to electronic books, Computers in Human Behavior 28 (4) (2012) 1186–1193.