TY - GEN
T1 - Autonomously revising knowledge-based recommendations through item and user information
AU - Rosenfeld, Avi
AU - Levy, Aviad
AU - Yoskovitz, Asher
PY - 2013
Y1 - 2013
N2 - Recommender systems are now an integral part of many e-commerce websites, providing people relevant products they should consider purchasing. To date, many types of recommender systems have been proposed, with major categories belonging to item-based, user-based (collaborative) or knowledge-based algorithms. In this paper, we present a hybrid system that combines a knowledge based (KB) recommendation approach with a learning component that constantly assesses and updates the system's recommendations based on a collaborative and item based components. This combination facilitated creating a commercial system that was originally deployed as a KB system with only limited user data, but grew into a progressively more accurate system by using accumulated user data to augment the KB weights through item based and collaborative elements. This paper details the algorithms used to create the hybrid recommender, and details its initial pilot in recommending alternative products in an online shopping environment.
AB - Recommender systems are now an integral part of many e-commerce websites, providing people relevant products they should consider purchasing. To date, many types of recommender systems have been proposed, with major categories belonging to item-based, user-based (collaborative) or knowledge-based algorithms. In this paper, we present a hybrid system that combines a knowledge based (KB) recommendation approach with a learning component that constantly assesses and updates the system's recommendations based on a collaborative and item based components. This combination facilitated creating a commercial system that was originally deployed as a KB system with only limited user data, but grew into a progressively more accurate system by using accumulated user data to augment the KB weights through item based and collaborative elements. This paper details the algorithms used to create the hybrid recommender, and details its initial pilot in recommending alternative products in an online shopping environment.
UR - http://www.scopus.com/inward/record.url?scp=84872477959&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34889-1_5
DO - 10.1007/978-3-642-34889-1_5
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AN - SCOPUS:84872477959
SN - 9783642348884
T3 - Lecture Notes in Business Information Processing
SP - 57
EP - 70
BT - Agent-Mediated Electronic Commerce - Designing Trading Strategies and Mechanisms for Electronic Markets, TADA 2011, Revised Selected Papers
PB - Springer Verlag
T2 - 2nd International Workshop on Trading Agent Design and Analysis, TADA 2011, Co-located with the 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Y2 - 17 July 2011 through 17 July 2011
ER -