ESLI: Enhancing slope one recommendation through local information embedding

Autoři: Heng-Ru Zhang aff001;  Yuan-Yuan Ma aff001;  Xin-Chao Yu aff001;  Fan Min aff001
Působiště autorů: School of Computer Science, Southwest Petroleum University, Chengdu, China aff001
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222702


Slope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommendation through local information embedding. First, we employ clustering algorithms to obtain the user clusters as well as item clusters to represent local information. Second, we predict ratings using the local information of users and items in the same cluster. The local information can detect strong localized associations shared within clusters. Third, we design different fusion approaches based on the local information embedding. In this way, both under-fitting and over-fitting problems are alleviated. Experiment results on the real datasets show that our approaches defeats slope one in terms of both mean absolute error and root mean square error.

Klíčová slova:

Clustering algorithms – Experimental design – Habits – k means clustering – Learning – Mathematical functions – Neural networks


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Článek vyšel v časopise


2019 Číslo 10