Categorization for grouping associative items using data mining in item-based collaborative filtering

Chung, Kyungyong; Lee, Daesung; Kim, Kuinam J.

doi:10.1007/s11042-011-0885-z

Cited by 25 publications

(12 citation statements)

References 5 publications

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“…User-based CF and item-based CF are recruited for highquality service recommendation in [4] and [5], respectively. To combine their advantages, a hybrid CF recommendation approach is proposed in [6].…”

Section: A Recommendation Accuracymentioning

confidence: 99%

“…Next, we utilize NB Set to make service recommendations to u target . Concretely, we predict service ws's quality over dimension q by u target , i.e., ws.q target based on (5). Here, ws is a service in cp (here, cp denotes the cloud platform that intends to recommend its own services to u target ) but never invoked by u target before, and ws.q j denotes ws' quality over dimension q observed by user u j .…”

Section: B Serrec Distri-lsh : Service Recommendation Based On Distrmentioning

confidence: 99%

See 1 more Smart Citation

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

Zhang

Dou

et al. 2017

IEEE J. Select. Areas Commun.

194

143

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Abstract-To maximize the economic benefits, a cloud service provider needs to recommend its services to as many users as possible based on the historical user-service quality data. However, when a cloud platform (e.g., Amazon) intends to make a service recommendation decision, considering only its own user-service quality data is insufficient because a cloud user may invoke services from multiple distributed cloud platforms (e.g., Amazon and IBM). In this situation, it is promising for Amazon to collaborate with other cloud platforms (e.g., IBM) to utilize the integrated data for the service recommendation to improve the recommendation accuracy. However, two challenges are present in the above collaboration process, where we attempt to use multi-source data for the service recommendation. First, protecting users' privacy is challenging when IBM releases its own data to Amazon. Second, the recommendation efficiency and scalability are often low when the user-service quality data of Amazon and IBM update frequently. Considering these challenges, a privacy-preserving and scalable service recommendation approach based on distributed locality-sensitive hashing (LSH), i.e., SerRec distri-LSH , is proposed in this paper to handle the service recommendation in a distributed cloud environment. Extensive experiments on the WS-DREAM dataset validate the feasibility of our approach in terms of service recommendation accuracy, scalability and privacy preservation.

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Section: A Recommendation Accuracymentioning

confidence: 99%

Section: B Serrec Distri-lsh : Service Recommendation Based On Distrmentioning

confidence: 99%

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

Zhang

Dou

et al. 2017

IEEE J. Select. Areas Commun.

194

143

View full text Add to dashboard Cite

show abstract

“…is approach is a common commercial recommendation method. Service recommendation systems usually utilize this method to calculate service similarities and make prediction [38]. (iii) UIPCC.…”

Section: Comparison Studymentioning

confidence: 99%

From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

Wen

Wang

2019

Mobile Information Systems

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With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users' personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. erefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users' location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users' reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users' reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. e evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.

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“…Collaborative filtering is a promising recommendation technique widely adopted in existing recommender systems, e.g., item-based collaborative filtering [15]. Generally, the CF recommendation methods can first look for the users who are similar with a target user or look for the services which are similar to a target service, based on the historical service quality data; afterward, the appropriate services that may be preferred by the target user are filtered out and put into the final recommended list.…”

Section: Accuracy-oriented Web Service Recommendationsmentioning

confidence: 99%

Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records

Wei

Wang²,

Zhang

et al. 2019

J Wireless Com Network

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In the age of big data, service recommendation has provided an effective manner to filter valuable information from massive data. Generally, by observing the past service invocation records (Boolean values) distributed across different cloud platforms, a recommender system can infer personalized preferences of a user and recommend him/her new services to gain more profits. However, the historical service invocation records are a kind of private information for users. Therefore, how to protect sensitive user data distributed across multiple cloud platforms is becoming a necessity for successful service recommendations. Additionally, the historical service invocation records often update with time, which call for an efficient and scalable service recommendation method. In view of these challenges, we introduce the multi-probe Simhash technique in information retrieval domain into the recommendation process and further put forward a privacy-preserving recommendation method based on historical service invocation records. At last, we design several experiments on the real-world service quality data in set WS-DREAM. Experimental results show the feasibility of the proposal in terms of producing accurate recommended results while protecting users' private information contained in historical service invocation records.

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Categorization for grouping associative items using data mining in item-based collaborative filtering

Cited by 25 publications

References 5 publications

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records

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