A Collaborative Filtering Recommendation Algorithm Based on Hierarchical Structure and Time Awareness

Tinghuai MA; Limin GUO; Meili TANG; Yuan TIAN; Mznah AL-RODHAAN; Abdullah AL-DHELAAN

doi:10.1587/transinf.2015EDP7380

A Collaborative Filtering Recommendation Algorithm Based on Hierarchical Structure and Time Awareness

Tinghuai MA, Limin GUO, Meili TANG, Yuan TIAN, Mznah AL-RODHAAN, Abdullah AL-DHELAAN

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User-based and item-based collaborative filtering (CF) are two of the most important and popular techniques in recommender systems. Although they are widely used, there are still some limitations, such as not being well adapted to the sparsity of data sets, failure to consider the hierarchical structure of the items, and changes in users' interests when calculating the similarity of items. To overcome these shortcomings, we propose an evolutionary approach based on hierarchical structure for dynamic recommendation system named Hierarchical Temporal Collaborative Filtering (HTCF). The main contribution of the paper is displayed in the following two aspects. One is the exploration of hierarchical structure between items to improve similarity, and the other is the improvement of the prediction accuracy by utilizing a time weight function. A unique feature of our method is that it selects neighbors mainly based on hierarchical structure between items, which is more reliable than co-rated items utilized in traditional CF. To the best of our knowledge, there is little previous work on researching CF algorithm by combining object implicit or latent object-structure relations. The experimental results show that our method outperforms several current recommendation algorithms on recommendation accuracy (in terms of MAE).

Publication: IEICE TRANSACTIONS on Information Vol.E99-D No.6 pp.1512-1520

Publication Date: 2016/06/01

Publicized: 2016/03/09

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2015EDP7380

Type of Manuscript: PAPER

Category: Data Engineering, Web Information Systems

Authors

Tinghuai MA
  Nanjing University of Information Science & Technology
Limin GUO
  Nanjing University of Information Science & Technology
Meili TANG
  Nanjing University of Information Science & Technology
Yuan TIAN
  King Saud University
Mznah AL-RODHAAN
  King Saud University
Abdullah AL-DHELAAN
  King Saud University

Keyword

recommender system, collaborative filtering, hierarchical structure, time weight

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Tinghuai MA, Limin GUO, Meili TANG, Yuan TIAN, Mznah AL-RODHAAN, Abdullah AL-DHELAAN, "A Collaborative Filtering Recommendation Algorithm Based on Hierarchical Structure and Time Awareness" in IEICE TRANSACTIONS on Information, vol. E99-D, no. 6, pp. 1512-1520, June 2016, doi: 10.1587/transinf.2015EDP7380.
Abstract: User-based and item-based collaborative filtering (CF) are two of the most important and popular techniques in recommender systems. Although they are widely used, there are still some limitations, such as not being well adapted to the sparsity of data sets, failure to consider the hierarchical structure of the items, and changes in users' interests when calculating the similarity of items. To overcome these shortcomings, we propose an evolutionary approach based on hierarchical structure for dynamic recommendation system named Hierarchical Temporal Collaborative Filtering (HTCF). The main contribution of the paper is displayed in the following two aspects. One is the exploration of hierarchical structure between items to improve similarity, and the other is the improvement of the prediction accuracy by utilizing a time weight function. A unique feature of our method is that it selects neighbors mainly based on hierarchical structure between items, which is more reliable than co-rated items utilized in traditional CF. To the best of our knowledge, there is little previous work on researching CF algorithm by combining object implicit or latent object-structure relations. The experimental results show that our method outperforms several current recommendation algorithms on recommendation accuracy (in terms of MAE).
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7380/_p

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@ARTICLE{e99-d_6_1512,
author={Tinghuai MA, Limin GUO, Meili TANG, Yuan TIAN, Mznah AL-RODHAAN, Abdullah AL-DHELAAN, },
journal={IEICE TRANSACTIONS on Information},
title={A Collaborative Filtering Recommendation Algorithm Based on Hierarchical Structure and Time Awareness},
year={2016},
volume={E99-D},
number={6},
pages={1512-1520},
abstract={User-based and item-based collaborative filtering (CF) are two of the most important and popular techniques in recommender systems. Although they are widely used, there are still some limitations, such as not being well adapted to the sparsity of data sets, failure to consider the hierarchical structure of the items, and changes in users' interests when calculating the similarity of items. To overcome these shortcomings, we propose an evolutionary approach based on hierarchical structure for dynamic recommendation system named Hierarchical Temporal Collaborative Filtering (HTCF). The main contribution of the paper is displayed in the following two aspects. One is the exploration of hierarchical structure between items to improve similarity, and the other is the improvement of the prediction accuracy by utilizing a time weight function. A unique feature of our method is that it selects neighbors mainly based on hierarchical structure between items, which is more reliable than co-rated items utilized in traditional CF. To the best of our knowledge, there is little previous work on researching CF algorithm by combining object implicit or latent object-structure relations. The experimental results show that our method outperforms several current recommendation algorithms on recommendation accuracy (in terms of MAE).},
keywords={},
doi={10.1587/transinf.2015EDP7380},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - A Collaborative Filtering Recommendation Algorithm Based on Hierarchical Structure and Time Awareness
T2 - IEICE TRANSACTIONS on Information
SP - 1512
EP - 1520
AU - Tinghuai MA
AU - Limin GUO
AU - Meili TANG
AU - Yuan TIAN
AU - Mznah AL-RODHAAN
AU - Abdullah AL-DHELAAN
PY - 2016
DO - 10.1587/transinf.2015EDP7380
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2016
AB - User-based and item-based collaborative filtering (CF) are two of the most important and popular techniques in recommender systems. Although they are widely used, there are still some limitations, such as not being well adapted to the sparsity of data sets, failure to consider the hierarchical structure of the items, and changes in users' interests when calculating the similarity of items. To overcome these shortcomings, we propose an evolutionary approach based on hierarchical structure for dynamic recommendation system named Hierarchical Temporal Collaborative Filtering (HTCF). The main contribution of the paper is displayed in the following two aspects. One is the exploration of hierarchical structure between items to improve similarity, and the other is the improvement of the prediction accuracy by utilizing a time weight function. A unique feature of our method is that it selects neighbors mainly based on hierarchical structure between items, which is more reliable than co-rated items utilized in traditional CF. To the best of our knowledge, there is little previous work on researching CF algorithm by combining object implicit or latent object-structure relations. The experimental results show that our method outperforms several current recommendation algorithms on recommendation accuracy (in terms of MAE).
ER -