A Collaborative Filtering Algorithm Based on Double Clustering and User Trust

Tang, Tonglong; Li, Xiaoyu

doi:10.2991/icsnce-16.2016.8

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…The author in this [12] incorporated social trust of users into the recommender system and developed a belief system between them. A CFL technique based on dual clustering and user belief was proposed to handle data sparsity and cold start problems [13]. The authors suggested a strategy based on different alternative viewpoints of reliability metrics in this paper [14] to increase the data sparsity issue.…”

Section: Related Workmentioning

confidence: 99%

“…The neighbor set is then built based on the user similarity measures by picking the first L users who are closest to user u. We may also calculate the prediction value using the following Eq (13):…”

Section: Simðu; Vþmentioning

confidence: 99%

“…• Secondly, the ratio of weight coefficient to weight of the local neighbors is computed [13][14][15].…”

Section: Plos Onementioning

confidence: 99%

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UsCoTc: Improved Collaborative Filtering (CFL) recommendation methodology using user confidence, time context with impact factors for performance enhancement

Mahesh

Kumar²,

Lim³

2023

PLoS ONE

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In today’s society, time is considered more valuable than money, and researchers often have limited time to find relevant papers for their research. Identifying and accessing essential information can be a challenge in this situation. To address this, the personalized suggestion system has been developed, which uses a user’s behavior data to suggest relevant items. The collaborative filtering strategy has been used to provide a user with the top research articles based on their queries and similarities with other users’ questions, thus saving time by avoiding time-consuming searches. However, when rating data is abundant but sparse, the usual method of determining user similarity is relatively straightforward. Furthermore, it fails to account for changes in users’ interests over time resulting in poor performance. This research proposes a new similarity measure approach that takes both user confidence and time context into account to increase user similarity computation. The experimental results show that the proposed technique works well with sparse data, and improves accuracy by 16.2% compared to existing models, especially during prediction. Furthermore, it enhances the quality of recommendations.

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Section: Related Workmentioning

confidence: 99%

Section: Simðu; Vþmentioning

confidence: 99%

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UsCoTc: Improved Collaborative Filtering (CFL) recommendation methodology using user confidence, time context with impact factors for performance enhancement

Mahesh

Kumar²,

Lim³

2023

PLoS ONE

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show abstract

“…And, due to the open nature of collaborative recommender systems, recommender systems cannot effectively prevent malicious users from injecting fake profile data into the ratings database; Zhang F introduced the social trust of the users into the recommender system and built the trust between them [14]. In order to solve data sparse and cold start problem, Tonglong Tang presented a collaborative filtering algorithm based on double clustering and user trust, which used user-clustering matrix to measure the user's degree of similarity [15]. And, Sajad Ahmadian proposed a method which is based on three different views of reliability measures to improve the data sparse and cold start problem [16].…”

Section: Related Workmentioning

confidence: 99%

A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

Tang

et al. 2019

Journal of Electrical and Computer Engineering

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Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

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“…There must have suitable applications running in cloud platform so that the cloud computing can exert into full play [45]. But how to compile parallel program which can run in cloud platform is very particular.…”

Section: Introductionmentioning

confidence: 99%