Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

Lianyong QI; Zhili ZHOU; Jiguo YU; Qi LIU

doi:10.1587/transinf.2016EDP7490

IEICE TRANSACTIONS on Information

Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

Lianyong QI, Zhili ZHOU, Jiguo YU, Qi LIU

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With the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, CF-based recommendation approaches can work well, when a target user has similar friends or the target services (i.e., services preferred by the target user) have similar services. However, when the available user-service rating data is very sparse, it is possible that a target user has no similar friends and the target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result. In view of this challenge, we combine Social Balance Theory (abbreviated as SBT; e.g., “enemy's enemy is a friend” rule) and CF to put forward a novel data-sparsity tolerant recommendation approach Ser_Rec_SBT+CF. During the recommendation process, a pruning strategy is adopted to decrease the searching space and improve the recommendation efficiency. Finally, through a set of experiments deployed on a real web service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy, recall and efficiency. The experiment results show that our proposed Ser_Rec_SBT+CF approach outperforms other up-to-date approaches.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.9 pp.2092-2099

Publication Date: 2017/09/01

Publicized: 2017/06/06

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016EDP7490

Type of Manuscript: PAPER

Category: Data Engineering, Web Information Systems

Authors

Lianyong QI
  Qufu Normal University
Zhili ZHOU
  Nanjing University of Information Science and Technology
Jiguo YU
  Qufu Normal University
Qi LIU
  Nanjing University of Information Science and Technology

Keyword

service recommendation, sparse data, enemy user, social balance theory, collaborative filtering

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Lianyong QI, Zhili ZHOU, Jiguo YU, Qi LIU, "Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 9, pp. 2092-2099, September 2017, doi: 10.1587/transinf.2016EDP7490.
Abstract: With the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, CF-based recommendation approaches can work well, when a target user has similar friends or the target services (i.e., services preferred by the target user) have similar services. However, when the available user-service rating data is very sparse, it is possible that a target user has no similar friends and the target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result. In view of this challenge, we combine Social Balance Theory (abbreviated as SBT; e.g., “enemy's enemy is a friend” rule) and CF to put forward a novel data-sparsity tolerant recommendation approach Ser_Rec_SBT+CF. During the recommendation process, a pruning strategy is adopted to decrease the searching space and improve the recommendation efficiency. Finally, through a set of experiments deployed on a real web service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy, recall and efficiency. The experiment results show that our proposed Ser_Rec_SBT+CF approach outperforms other up-to-date approaches.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7490/_p

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@ARTICLE{e100-d_9_2092,
author={Lianyong QI, Zhili ZHOU, Jiguo YU, Qi LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering},
year={2017},
volume={E100-D},
number={9},
pages={2092-2099},
abstract={With the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, CF-based recommendation approaches can work well, when a target user has similar friends or the target services (i.e., services preferred by the target user) have similar services. However, when the available user-service rating data is very sparse, it is possible that a target user has no similar friends and the target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result. In view of this challenge, we combine Social Balance Theory (abbreviated as SBT; e.g., “enemy's enemy is a friend” rule) and CF to put forward a novel data-sparsity tolerant recommendation approach Ser_Rec_SBT+CF. During the recommendation process, a pruning strategy is adopted to decrease the searching space and improve the recommendation efficiency. Finally, through a set of experiments deployed on a real web service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy, recall and efficiency. The experiment results show that our proposed Ser_Rec_SBT+CF approach outperforms other up-to-date approaches.},
keywords={},
doi={10.1587/transinf.2016EDP7490},
ISSN={1745-1361},
month={September},}

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TY - JOUR
TI - Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering
T2 - IEICE TRANSACTIONS on Information
SP - 2092
EP - 2099
AU - Lianyong QI
AU - Zhili ZHOU
AU - Jiguo YU
AU - Qi LIU
PY - 2017
DO - 10.1587/transinf.2016EDP7490
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2017
AB - With the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, CF-based recommendation approaches can work well, when a target user has similar friends or the target services (i.e., services preferred by the target user) have similar services. However, when the available user-service rating data is very sparse, it is possible that a target user has no similar friends and the target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result. In view of this challenge, we combine Social Balance Theory (abbreviated as SBT; e.g., “enemy's enemy is a friend” rule) and CF to put forward a novel data-sparsity tolerant recommendation approach Ser_Rec_SBT+CF. During the recommendation process, a pruning strategy is adopted to decrease the searching space and improve the recommendation efficiency. Finally, through a set of experiments deployed on a real web service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy, recall and efficiency. The experiment results show that our proposed Ser_Rec_SBT+CF approach outperforms other up-to-date approaches.
ER -

IEICE TRANSACTIONS on Information