@article{Zhang2018, 
author = {Chunxia Zhang and Ming Yang and Jing Lv and Wanqi Yang},
title = {An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor},
year = {2018},
journal = {Big Data Mining and Analytics},
volume = {1},
number = {2},
pages = {128-136},
keywords = {recommendation system, similarity, tag, time factor},
url = {https://www.sciopen.com/article/10.26599/BDMA.2018.9020012},
doi = {10.26599/BDMA.2018.9020012},
abstract = {The Collaborative Filtering (CF) recommendation algorithm, one of the most popular algorithms in Recommendation Systems (RS), mainly includes memory-based and model-based methods. When performing rating prediction using a memory-based method, the approach used to measure the similarity between users or items can significantly influence the recommendation performance. Traditional CFs suffer from data sparsity when making recommendations based on a rating matrix, and cannot effectively capture changes in user interest. In this paper, we propose an improved hybrid collaborative filtering algorithm based on tags and a time factor (TT-HybridCF), which fully utilizes tag information that characterizes users and items. This algorithm utilizes both tag and rating information to calculate the similarity between users or items. In addition, we introduce a time weighting factor to measure user interest, which changes over time. Our experimental results show that our method alleviates the sparsity problem and demonstrates promising prediction accuracy.}
}