BigDataFr recommends: LLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios

Subjects: Machine Learning (stat.ML); Information Retrieval (cs.IR); Social and Information Networks (cs.SI)

[…]The purpose if this master’s thesis is to study and develop a new algorithmic framework for Collaborative Filtering to produce recommendations in the top-N recommendation problem. Thus, we propose Lanczos Latent Factor Recommender (LLFR); a novel « big data friendly » collaborative filtering algorithm for top-N recommendation. Using a computationally efficient Lanczos-based procedure, LLFR builds a low dimensional item similarity model, that can be readily exploited to produce personalized ranking vectors over the item space. A number of experiments on real datasets indicate that LLFR outperforms other state-of-the-art top-N recommendation methods from a computational as well as a qualitative perspective. Our experimental results also show that its relative performance gains, compared to competing methods, increase as the data get sparser, as in the Cold Start Problem.[…]

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By Maria Kalantzi
Source: arxiv.org