Study on Sparsity of Recommender System in University Library

DOI

10.2991/ncce-18.2018.73How to use a DOI?

Keywords

Book Recommender Systems, Model-based Collaborative Filtering, Sparsity, Logarithmic Transformation.

Abstract

Due to the lack of readers’ rating data, university library recommender system is facing the problem of sparsity of data. This study proposes a general logarithmic transformation model which can convert the reader’s implicit feedback data to the rating, thus alleviating the sparsity to a certain extent. Because logarithmic transformation can use different base, several logarithmic transformation methods are analyzed and compared from different angles. The model-based collaborative filtering is used to compare the recall and accuracy of these methods to make full use of the technical advantage based on matrix factorization to further alleviate the data sparsity problem. The experimental results show that the proposed general logarithmic transformation model can play the role of modifying the data skew, compressing the variable scale, reducing the value of the calculation and so on, and the results of the model are interpretable. Moreover, when the suitable k value is selected, the recommended results of different logarithmic transformation methods can approach the optimal solution in a finite experiment.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xu Zhao
AU  - Guang Liu
PY  - 2018/05
DA  - 2018/05
TI  - Study on Sparsity of Recommender System in University Library
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 470
EP  - 476
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.73
DO  - 10.2991/ncce-18.2018.73
ID  - Zhao2018/05
ER  -