Recommendation with Generalized Logistic Transformation

Zhuo-Lin Fu
Zhuo-Lin Fu
Heng-Ru Zhang
Heng-Ru Zhang

ICBK, pp. 390-399, 2018.

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Abstract:

Many recommender systems explicitly or implicitly assume that rating data are normally distributed. This assumption is handy, but often does not hold in practice, resulting in system underperformance. In this paper, we design a recommendation algorithm embedding a new distribution model. First, we introduce a generalized logistic transfor...More

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