White box : on the prediction of collaborative filtering recommendation systems' performance

Paun, Iulia and Moshfeghi, Yashar and Ntarmos, Nikos (2022) White box : on the prediction of collaborative filtering recommendation systems' performance. ACM Transactions on Internet Technology. ISSN 1533-5399 (https://doi.org/10.1145/3554979)

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Abstract

Collaborative Filtering recommendation algorithms (CF) are a popular solution to the information overload problem, aiding users in the item selection process. Relevant research has long focused on refining and improving these models to produce better (more effective) recommendations, and has converged on a methodology to predict their effectiveness on target datasets by evaluating them on random samples of the latter. However, predicting the efficiency of the solutions – especially with regards to their time- and resource-hungry training phase, whose requirements dwarf those of the prediction/recommendation phase – has received little to no attention in the literature. This paper addresses this gap for a number of representative and highly popular CF models, including algorithms based on matrix factorisation, k-nearest neighbours, co-clustering, and slope one schemes. To this end, we first study the computational complexity of the training phase of said CF models and derive time and space complexity equations. Then, using characteristics of the input and the aforementioned equations, we contribute a methodology for predicting the processing time and memory usage of their training phase. Our contributions further include an adaptive sampling strategy, to address the trade-off between resource usage costs and prediction accuracy, and a framework which quantifies both the efficiency and effectiveness of CF. Finally, a systematic experimental evaluation demonstrates that our method outperforms state-of-the-art regression schemes by a considerable margin, with an overhead that is a small fraction of the overall requirements of CF training.

  • Item type:Article
    ID code:81944
    Dates:
    Date
    Event
    12 August 2022
    Published
    12 August 2022
    Published Online
    21 July 2022
    Accepted
    Keywords:recommendation systems, efficiency evaluation, effectiveness evaluation, sampling-based time and memory prediction, Bibliography. Library Science. Information Resources, Library and Information Sciences
    Subjects:Bibliography. Library Science. Information Resources
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:19 Aug 2022 13:19
    Last modified:18 Jan 2023 11:40
    URI:https://strathprints.strath.ac.uk/id/eprint/81944
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