Accelerating Parallel ALS for Collaborative Filtering on Hadoop

Liang, Yi; Zeng, Shaokang; Yan-de, Liang; Chen, Kaizhong

doi:10.1007/978-3-030-49556-5_13

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2020

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“…ALS has the advantage of being easily parallelized. Some recent studies focused on speeding up the implementation of this algorithm [42,43]. Another study developed a recommender system for movies based on ALS using Apache Spark [44].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Semantic Recommender System for Chemical Compounds in Large-Scale Datasets

Barros

Moitinho

Couto

2020

Preprint

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The large, and increasing, number of chemical compounds poses challenges to the exploration of such datasets. In this work, we propose the use of Recommender Systems in the selection of compounds of interest to scientific researchers. Our approach consists of a Hybrid recommender model suitable for implicit feedback datasets and focused on retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares and Bayesian Personalized Ranking) and a new content-based algorithm, based on the semantic similarity of the chemical compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of chemical compounds, CheRM-20, with more than 16.000 items (chemical compounds). The Hybrid model was able to improve the results of the collaborative-filtering algorithms, with increases of more than ten percentage points in most of the assessed evaluation metrics.

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Section: Introductionmentioning

confidence: 99%