Recommender System for Books in University Library with Implicit Data

DOI

10.2991/ncce-18.2018.28How to use a DOI?

Keywords

Implicit Feedback, Explicit Representation, SVD++, Model-based Collaborative Filtering, Book Recommender Systems.

Abstract

Recommender system is a very important tool to help customers make choices more easily in a large variety of offered products. However, it is difficult to make directly use of the recommender system to provide suggestion for the traditional books in a library because of the shortage of the explicit feedback, like readers’ rating, reviews etc. We propose a model that transfers the implicit data of readers borrow history to explicit data and apply the SVD++ algorithm in the recommender system.

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  - Guang Liu
AU  - Xu Zhao
PY  - 2018/05
DA  - 2018/05
TI  - Recommender System for Books in University Library with Implicit Data
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 164
EP  - 168
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.28
DO  - 10.2991/ncce-18.2018.28
ID  - Liu2018/05
ER  -