Spectrum auction is an important approach of spectrum distribution in cognitive radio networks. However, a single secondary user(SU) probably can't afford the price of spectrum. Multiple SUs grouping together to p...Spectrum auction is an important approach of spectrum distribution in cognitive radio networks. However, a single secondary user(SU) probably can't afford the price of spectrum. Multiple SUs grouping together to participate in the auction as a whole is helpful to increase purchasing power. However, SUs could suffer from a new group cheating problem, i.e., parts of users conspire to manipulate the auction by submitting untruthful bids. Existing auction mechanisms were mainly designed to be strategy-proof only for individual user and can't deal with group cheating. In this paper, a novel spectrum auction mechanism called COSTAG(COst Sharing based Truthful Auction with Group-buying) is proposed to address the group cheating problem. COSTAG consists of a grouping rule to perform grouping and a payment rule to determine the market-clearing price in the spectrum auction. Different from single-echelon pricing approach employed in existing works, a multi-echelon pricing strategy is designed to increase the transaction rate and optimize social profit for the auction. Comprehensive theoretical analysis shows that COSTAG can satisfy the crucial economic robustness properties, both individual and group truthfulness. Simulations demonstrate that comparing with existing works, COSTAG can improve the system performance significantly.展开更多
Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most ...Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.展开更多
基金partially supported by the National Science Foundation of China (No. 61070211, No. 61003304, No 61501482 and No 61070201)Equipment research foundation (No.6140134040216)the Ph.D. Programs Foundation of Ministry of Education of China (No. 20114307120003)
文摘Spectrum auction is an important approach of spectrum distribution in cognitive radio networks. However, a single secondary user(SU) probably can't afford the price of spectrum. Multiple SUs grouping together to participate in the auction as a whole is helpful to increase purchasing power. However, SUs could suffer from a new group cheating problem, i.e., parts of users conspire to manipulate the auction by submitting untruthful bids. Existing auction mechanisms were mainly designed to be strategy-proof only for individual user and can't deal with group cheating. In this paper, a novel spectrum auction mechanism called COSTAG(COst Sharing based Truthful Auction with Group-buying) is proposed to address the group cheating problem. COSTAG consists of a grouping rule to perform grouping and a payment rule to determine the market-clearing price in the spectrum auction. Different from single-echelon pricing approach employed in existing works, a multi-echelon pricing strategy is designed to increase the transaction rate and optimize social profit for the auction. Comprehensive theoretical analysis shows that COSTAG can satisfy the crucial economic robustness properties, both individual and group truthfulness. Simulations demonstrate that comparing with existing works, COSTAG can improve the system performance significantly.
基金supported by the National Basic Research Program(973)of China(No.2012CB316400)the National Natural Science Foundation of China(No.61571393)
文摘Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.
