A Collaborative Filtering Method for Personalized Preference-Based Service Recommendation

Fletcher, Kenneth K.; Liu, Xiaoqing Frank

doi:10.1109/icws.2015.60

Cited by 39 publications

(8 citation statements)

References 12 publications

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“…Previous CF-based QoS prediction approaches (e.g., [7,35]) only utilize the available QoS values in collaborative filtering model to make personalized service recommendation. However, these methods ignore that users' reputation and location will make great impact on the prediction results.…”

Section: Principles Of Lrmfmentioning

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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Section: Principles Of Lrmfmentioning

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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“…While the abovementioned CF variants have an obvious shortcoming, i.e., they only recruit objective decision data (e.g., historical service quality) for recommendations, while neglecting other key factors that may affect a target user's recommendation decision-makings, e.g., users' personalized preferences. In view of this shortcoming, CF recommendation methods are improved in [18] by considering the preferences of users, to support personalized and preference-aware service selection decisions of different users.…”

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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“…The mainstream recommendation algorithms can be classified as content-based [2,3], collaborative filtering [5,25], knowledge-based [6] and hybrid ones [7]. The collaborative filtering methods recommend items to users by exploiting the taste of other similar users.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Recommendation for Music Based on Reinforcement Learning

Wang

2020

Advances in Knowledge Discovery and Data Mining

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The key to personalized recommendation system is the prediction of users' preferences. However, almost all existing music recommendation approaches only learn listeners' preferences based on their historical records or explicit feedback, without considering the simulation of interaction process which can capture the minor changes of listeners' preferences sensitively. In this paper, we propose a personalized hybrid recommendation algorithm for music based on reinforcement learning (PHRR) to recommend song sequences that match listeners' preferences better. We firstly use weighted matrix factorization (WMF) and convolutional neural network (CNN) to learn and extract the song feature vectors. In order to capture the changes of listeners' preferences sensitively, we innovatively enhance simulating interaction process of listeners and update the model continuously based on their preferences both for songs and song transitions. The extensive experiments on real-world datasets validate the effectiveness of the proposed PHRR on song sequence recommendation compared with the state-of-the-art recommendation approaches.

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A Collaborative Filtering Method for Personalized Preference-Based Service Recommendation

Cited by 39 publications

References 12 publications

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

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

A Hybrid Recommendation for Music Based on Reinforcement Learning

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