Collaborative Filtering based Recommender System using Regression and Grey Wolf Optimization Algorithm for Sparse Data

Sneha, V.; Shrinidhi, K.R.; Sunitha, R; Nair, Mydhili K.

doi:10.1109/icces45898.2019.9002394

Cited by 5 publications

(1 citation statement)

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“…On the basis of these criteria, RS algorithms can be distinguished as (i) Content-based (CBF) RS (ii) Collaborative Filtering (CF) based RS and (iii) Hybrid RS. CBF uses characteristic information for recommendation [10] and CF systems works based on the relation among user-item [11]. Hybrid systems combine both types of information to avoid demerits that occur when using a single type [12].…”

Section: Introductionmentioning

confidence: 99%

Cold start aware hybrid recommender system approach for E-commerce users

et al. 2022

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Collaborative Filtering (CF) schemes are very popular in Recommender System (RS) and offer specialized suggestions to users in e-commerce and social websites. But, they suffer from the Cold Start Problem (CSP) that occurs due to the lack of sufficient information about the new customers, purchase history, and browsing data. Moreover, data sparsity problems may arise when the interaction is made among a limited amount of items. This not only poses a negative impact on recommendation but also significantly condenses the diversity of choices available in the particular platform. To tackle these issues, a novel methodological approach called Sparsity and Cold Start Aware Hybrid Recommended System (SCSHRS) is designed to suppress data sparsity and CSP in RS. The proposed SCSHRS methodology comprises four stages. At the initial stage, the data sparsity is reduced and at stage 2, the similar users are grouped by Ant-Lion based k-means clustering. At stage 3, Higher-Order Singular Value Decomposition (HOSVD) method decomposes the data to a lesser dimension. At the final stage, the Adaptive Neuro-Fuzzy Inference System (ANFIS) uses IF-THEN rules and machine learning abilities to predict the output. The performance of the proposed SCSHRS method is tested on MovieLens-20M, Last. FM, and Book-Crossing datasets and compared with the prevailing techniques.Based on the evaluation report, the proposed SCSHRS system gives Mean Absolute Percentage Error (MAPE) of 40%, and, precision (0.16), recall (0.08), F-measure (0.1), and Normalized Discounted Cumulative Gain (NDCD) of 0.65. Hence, SCSHRS is proved to be a more efficient means of recommendation against cold start and sparsity problems.

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Section: Introductionmentioning

confidence: 99%