Title: Slope one collaborative recommendations: a survey

Authors: Neeraj Kumar Bharti; Vijay Verma

Addresses: Computer Engineering Department, NIT Kurukshetra, India ' Computer Engineering Department, NIT Kurukshetra, India

Abstract: Collaborative filtering (CF) is a traditional and popular technique in the recommendation system (RS) paradigm. Notably, one specific form of item-based collaborative filtering (IBCF), known as the slope one algorithm, deals with data sparsity in many different ways. Slope one algorithms are simple; therefore, their implementations are more straightforward than other complicated IBCF approaches. This work summarises the state-of-the-art techniques for slope one recommendations. Various slope one predictors are analysed and compared with each other along with their pros and cons. Several experiments have been performed using different datasets such as MovieLens-100k, MovieLens-1M, and Filmtrust. Finally, the weighted slope one predictor is compared with the basic IBCF using mean absolute error (MAE) and root mean squared error (RMSE) metrics. Empirical values of MAE and RMSE demonstrate that the slope one predictors provide the almost same accuracy as obtained from other complex and computationally expensive methods.

Keywords: recommender system; slope one; collaborative filtering; item-based CF; data sparsity.

DOI: 10.1504/IJDS.2023.132284

International Journal of Data Science, 2023 Vol.8 No.3, pp.240 - 257

Received: 29 Sep 2021
Received in revised form: 05 Jun 2022
Accepted: 20 Oct 2022
Published online: 17 Jul 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article