k-Surrounding Neighbors: Incorporating Serendipity in Collaborative Recommendations

DOI

10.2991/978-94-6463-222-4_11How to use a DOI?

Keywords

Collaborative filtering; k-Nearest Neighbors; Serendipity; Recommender system

Abstract

Social recommender systems have become ubiquitous in our online environment, but there are growing concerns that they narrow our horizons and polarize our opinions. This paper proposes a new recommendation algorithm, k-Surrounding Neighbors, based on the theory of weak ties, to increase the diversity and novelty of recommendations. The proposed method discards some nearest neighbors based on their similarity, giving more weight to less similar others, which can provide fresh information and new experiences. Validation tests using several metrics show that it significantly improves recommendation diversity and novelty at a minor cost in precision.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Huidi Lu
PY  - 2023
DA  - 2023/08/28
TI  - k-Surrounding Neighbors: Incorporating Serendipity in Collaborative Recommendations
BT  - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
PB  - Atlantis Press
SP  - 115
EP  - 122
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-222-4_11
DO  - 10.2991/978-94-6463-222-4_11
ID  - Lu2023
ER  -