GPS: Factorized group preference-based similarity models for sparse sequential recommendation

Danial Hooshyar
Danial Hooshyar

Information Sciences, pp. 394-411, 2019.

Cited by: 3|Views5
EI

Abstract:

•One of the key tasks for recommender systems is the prediction of personalized sequential behavior.•There are two primary means of modeling sequential patterns and long-term user preferences: Markov chains and matrix factorization, respectively.•This proposed approach, called GPS (a factorized Group Preference-based Similarity model), fu...More

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