Recommended Items Rating Prediction based on RBF Neural Network Optimized by PSO Algorithm

Transcription

1 Recommended Items Ratng Predcton based on RBF Neural Network Optmzed by PSO Algorthm Chengfang Tan, Cayn Wang, Yuln L and Xx Q Abstract In order to mtgate the data sparsty and cold-start problems of recommendaton tem ratngs and more accurately predct the ratngs of recommended tems, accordng to the characterstcs of the non-lnearty and randomness n recommendaton content changes, recommended tems ratngs predcton model based on RBF neural network optmzed by PSO algorthm s proposed and the parameters of RBF neural network are globally optmzed by usng the proposed PSO algorthm. Expermental results show that, compared wth the state-of-the-art models ncludng the tradtonal user-based collaboratng flterng method, the tradtonal tem-based collaboratng flterng method and RBF neural network model, the proposed RBF neural network optmzed by PSO algorthm can more accurately predct the recommendaton tem ratngs, whch has hgher predcton accuracy and much lower predcton error measured by Root Mean Square Error (RMSE), Precson@N and Recall@N. Keywords PSO algorthm, RBF neural network, recommended tem ratng predcton, data sparsty. I. INTRODUCTION he rapd development of Internet trggers the problem of Tnformaton overload. As an mportant means to solve the problem of nformaton overload, the recommendaton system s wdely used n the felds of electronc commerce, manly flterng out the unrelated tems to allevate the burden of users. At present, the recommendaton system can be roughly categorzed nto the content-based recommendaton system and the collaboratve flterng recommendaton system. However, Ths work was supported by Key Projects of Excellent Young Talent Support Program n Colleges and Unverstes of Anhu Provnce (gxyqzd216344),unversty Students Innovatve Undertakng Tranng Program of Anhu Provnce ( , ), Teachng Research Project of Suzhou Unversty (szxy216jy32), the Key Projects of Anhu Provnce Colleges and Unverstes Natural Scence Foundaton of Chna (KJ216A768) and Soft Scence Research Project of Anhu Scence and Technology Plan n 216 (167a2271). Chengfang Tan s wth School of Informaton Engneerng, Suzhou Unversty, Suzhou 234, Anhu, Chna (correspondng author; e-mal: @qq.com). Cayn Wang s wth School of Informaton Engneerng, Suzhou Unversty, Suzhou 234, Anhu, Chna. Yuln L s wth School of Informaton Engneerng, Suzhou Unversty, Suzhou 234, Anhu, Chna. Xx Q s wth School of Informaton Engneerng, Suzhou Unversty, Suzhou 234, Anhu, Chna.. one of the bggest problems exstng n the collaboratve flterng recommendaton s that, as most users are not wllng to provde tem ratng nformaton or new tems do not have enough ratng data, the clusterng effect from smlar users s poor [1,2]. That s to say, under the naccurate clusterng class of smlar users, the cross recommendaton among users wll serously affect the recommendaton qualty and even produce wrong recommendaton, ultmately, data sparsty and cold start problems are produced. Amng at the above mentoned problems of collaboratve flterng recommendaton systems, many scholars have put forward ther vews from dfferent angles, and have acheved good results [3]. For example, reference 4 fused the cosne smlarty and Pearson correlaton wth Jaccard smlarty coeffcents method for the accurate measurement of the smlarty between users. In reference 5, the user's socal relaton s ntroduced nto the recommendaton system to realze the better recommendaton. Reference 6 combned the trust relatonshp between users wth the collaboratve flterng recommendaton, utlzed the explct trust relatonshp and the transton of trust relaton to calculate the trust degree between users, and selected the nearest neghbor based on the strength of trust relatonshp. Reference 7 ntegrated the matrx factorzaton model wth the trust relatonshp between users, exploted the nfluence and the transtvty of trust relatonshp to map users and tems nto the same feature space, then the preference predcton of users was performed n the feature space. Accordng to reference 8, the personal nformaton of users could reflect the user's nterest, and put forward adoptng the user's personal nformaton to complement the user's preference. Reference 9 put forward the concept of the same scorng matrx from users, based on the smlarty between the users and the same scorng matrx, the not evaluated tems were performed the preference predcton Motvated by the above observatons, we propose a new recommended tems ratng predcton method based on RBF (Radal Bass Functon) neural network optmzed by PSO algorthm (short for PSO-RBF). PSO algorthm not only has a strong global search capablty, but also s easy to be acheved. The parameters of RBF neural network optmzed by PSO algorthm are the global optmal parameters, whch can overcome the problem of low relablty of RBF neural network learnng and guarantee that the recommended tems ratngs are more accurately predcted. ISSN:

2 II. THE PROPOSED RECOMMENDED ITEMS RATING PREDICTION BASED ON RBF NEURAL NETWORK OPTIMIZED BY PSO ALGORITHM In the followng sectons, we frst ntroduce partcle swarm optmzaton algorthm and RBF neural network descrbed, respectvely. Then based on RBF neural network optmze by PSO algorthm, a PSO- recommended tems ratng predcton model s presented n detal. Lastly, we reports expermental results. 1. Partcle swarm optmzaton algorthm Partcle Swarm Optmzaton algorthm [1] s an evolutonary algorthm based on swarm ntellgence proposed by Kennedy and Eberhart n In PSO algorthm, the feasble soluton of each optmzaton problem s regarded as a partcle n the search space. Assume that there are N partcles n moton n D dmensonal space, v = ( v,1, v,2 vd, ) denotes the velocty vector of partcle, X= ( x,1, x,2 xd, ) represents the space poston vector of partcle. Partcle constantly adjusts ts speed and space poston accordng to v and X untl t reaches the global optmal soluton. Pbest denotes the optmal soluton of partcle tself and Pbest = ( p,1, p,2 p, D ). The optmal soluton for the populaton at present s expressed by g best and () t t t t g = ( g, g g ). best,1,2, D v = ( v,1, v,2 vd, ) s the velocty of partcle n tme t, then the speed of n the t + 1 th teraton s expressed as follows: ( t + 1) t ( t) ( t) v = wv + c1rand( )( Pb X ) + c2rand( )( gb X ) (1) Where w s the nerta factor and ts value s greater than, c1 s cogntve learnng factor and c 2 s socal learnng factor, c1 and c 2 are both postve constants, rand( ) s a functon that generates the random number rangng from to 1. The velocty value of the partcle s n the range of[ vmn, v max ]. If the velocty of a partcle s less than vmn or greater than v max, then the velocty value of the partcle s set to the correspondng threshold value. In the t + 1 th teraton, the poston of partcle s denoted as shown n Equaton (2), X = X + v (2) ( t+ 1) t ( t+ 1) Durng actually utlzng PSO algorthm, f the number of teratons reaches the maxmum value or the partcle fnally fnds the best optmal locaton n accordance wth the mnmum ftness threshold, then teraton s termnated [11]. 2. RBF neural network In 1998, Broomhead et al. proposed a radal bass functon based neural network (short for RBF) [12]. Smlar wth other neural networks, RBF neural network has a good local approxmaton, fast convergence and so on. The structure of RBF neural network s shown n Fg. 1 as follows: x 1 x 2 x n h 1 h m W y1 Hdden Layer Input Layer Output Layer Fg. 1. Structure of RBF neural network From Fg. 1, t can be observed that RBF neural network ncludes nput layer, hdden layer and output layer. It s a feed forward neural network wth the structure of three layers. Input layer can acheve the functon of smply transferrng the nput data nto the hdden layer, hdden layer neurons are able to make the nput data produce non-lnear mappng. Subsequently, neurons n the output layer perform lnear weghted operatons towards the nonlnear data output from the hdden layer, and ultmately results are output n a lnear style. As shown n Fg. 1, X = [ x1, x2,, x ] T n s an n-dmensonal nput vector, H = [ h1, h2,, h ] T m s a radal bass functons used n the hdden layer, whch denotes the output of hdden layer unt and s usually represented by usng the Gauss functon. The equaton s presented as follows, X C h ( x) = exp 2 = 1, 2,, m (3) 2b In Equaton (3), C represents the center of the frst th bass functon, b denotes the wdth of the th bass functon, and b s a number greater than. s the Eucldean paradgm, m denotes the number of nodes n the hdden layer. W = [ w1, w2,, w ] T m s the weghted vector of RBF neural network. The output of RBF neural network s a lnear combnaton of the output of the hdden layer nodes and s denoted as, y( x) = Wh( x) (4) k k k = 1 q In Equaton (4), k = 1, 2,, q and q s the number of nodes n the output layer, W k represents the connected weghts between the th node from the hdden layer and the node from the output layer. From the descrpton on RBF neural network, t can be vsble that, n the RBF neural network k th, Gauss bass functon center vectorc, Gauss bass functon wdth vector b, the number of radal bass functon center m and the connected weghts W k between the output layer and the hdden layer need to be determned. After determnng the above mentoned C, b and m, the least squares method can be used to drectly compute the connected weghts W k between the output layer and the hdden layer. C, b and W k y 2 y q ISSN:

3 determne the performance of the whole RBF neural network together. 3. The proposed recommended tems ratng predcton model From the descrpton of RBF neural network, t can be concluded that the predcton performance of RBF neural network manly depends on Gauss bass functon center vector C, Gauss bass functon wdth vector b, and connected weghts W k between the output layer and the hdden layer. However, the tradtonal RBF neural network adopts the local nformaton based on the parameter space to set the relatve parameters, and t causes that the values of parameter C, b and w k are the local optmal solutons, not the global optmal solutons. Amng at the above mentoned dsadvantages from RBF neural network, durng the process of predctng the changes of recommendaton tems ratng, we make use of PSO algorthm to perform the global optmzaton towards three parameters C, b and w k n RBF neural network. The proposed recommended tems ratng predcton algorthm based on RBF neural network optmzed by PSO algorthm (short for PSO-RBF) s ntroduced n detal as follows: Step 1: Intalze the partcle swarm and neural network. The pre-processed recommendaton samples are fed nto RBF neural network for tranng, and three above mentoned parameters C, b,and W k are obtaned. Parameters C, b and w k are respectvely regarded as the ntal poston of the partcle, the velocty of the partcle and the poston vector of the partcle to form one by one partcle. Step 2. Compute and evaluate the ftness value of each partcle. Suppose v and vˆ respectvely denote the true value and the predctve value of the recommended tem ratng at a certan tme. Assume that σ s the number of tranng examples, Ftness functon adpt s defned to measure the merts of the selecton parameters and ts equaton s denoted as follows, 1 adpt = σ = 1 v ˆ v v From Equaton (5), t can be represented that the negatve mean relatve error between the true value v and the predctve value vˆ of the recommended tem ratng s regard as a measure of the ftness value of each partcle. Step 3. Update the search poston of each partcle accordng to the ftness value of each ndvdual, and calculate the ndvdual extreme value pbest and the populaton global extreme value gbest of each partcle. The detaled operatons are as follows, for each partcle, we compare the ftness value of each partcle wth the best poston pbest that ths partcle has ever experenced, f the current ftness value of ths partcle s better, and then the current ftness value of the partcle s regarded as the current best poston pbest. For each partcle, (5) we compare the ftness value of each partcle wth the populaton global extreme gbest that all partcles have ever experenced, f t s better, and then gbest s set to the current poston of the best partcle. Step 4. Utlzng the characterstcs of PSO algorthm, the three parameters C, b and w k n RBF neural network are contnuously performed globally optmzed. If the teraton reaches the maxmum number or the mean square error reaches the ntal settng value, then the partcle search s termnated and the optmal partcle poston s output; otherwse returns step 3, the teratve optmzaton s executed repeatedly, untl the optmal values of the parameters are obtaned n RBF neural network. Step 5. Input the measured data to predct the change of the recommended tems ratng. The decoded value of the best poston that the swarm has experenced s regarded as the structural parameter of RBF neural network, and the decoded value s taken nto RBF neural network. We utlze another group of the recommended tems ratng to test the traned recommended tems ratng predcton model and the predcton effcency of the recommended tems s obtaned. III. EXPERIMENTAL RESULTS AND VERIFICATION In ths secton, we conduct comparatve experments to evaluate the effectveness and effcency of the proposed PSO- recommended tems ratng predcton model. Specfcally, we am to answer the followng two questons: (1) Can the proposed PSO- recommendaton method mprove the recommendaton performance by accurately predctng the ratng of recommended tems? (2) Is the proposed PSO- recommended method able to mtgate the cold-start problem for recommendaton by accurately predctng the ratng of tems? We begn by ntroducng two real-world datasets, the expermental settngs and the evaluaton metrcs we choose, then we compare the PSO- recommendaton method wth the three state-of-the-art baselnes to answer the frst queston and we explore the capablty of the proposed PSO-RBF method n handlng the cold-start problem to answer the second queston. All the algorthms were mplemented usng Java and all experments were performed on a server runnng Wndows Server 28 wth four 3.GHz CPU cores and 12GB memory. In ths paper, we explot the same ordnary data structures for all the algorthms and do not consder any parallel computaton. 1. Expermental datasets We collect two real-world datasets for the comparatve experments, among whch, one s from the socal meda webste Epnons ( that provdes the onlne servces for users, users can perform many nformaton sharng operatons on Epnons, for example, revewng and ratng the tems, etc. From the orgnally collected datasets, we flter out users who rated few tems and also tems that receved less than 1 ratngs. The fnal adopted dataset from Epnons s shown n Table 1. ISSN:

4 Table 1. Statstcal nformaton on Epnos Number of users Another dataset s from Movelens, whch ncludes about 1 mllon move ratng nformaton that 6,4 users rated the 3,952 moves wth scores from 1 to 5. The statstcs of the MoveLens dataset s shown n Table 2. Table 2. Statstcal nformaton on MoveLens Number Number Number of users of moves of ratngs MoveLens 6,4 3,952 About 1M 2. Evaluaton metrcs and expermental settng (1) Evaluaton Metrcs Frstly, we adopt the Root Mean Squared Error (RMSE) to measure the predcton error. RMSE s defned as follows [13]: 1 2 RMSE = ( rˆ j rj ) (6) N, j Where r j denotes the ratng of recommendaton tem j by user, rˆj denotes the correspondng ratng predcted by the model, and N denotes the total number of the test set. The smaller the value of RMSE, the more precse a recommendaton s. Secondly, rankng a recommendaton lst s often more mportant than the ratng predcton, so we also evaluate the algorthms n terms of rankng. To measure the accuracy of rankng a recommendaton lst, we adopt two standard metrcs Precson@N (denoted by P@N) and Recall@N (denoted by R@N). Specfcally, we thnk that a tem ratng s successfully predcted f t s n the recommendaton lst. P@N defnes the rato of successfully predcted tem topcs to the N recommendatons, and R@N defnes the rato of successfully predcted tem topcs to the number of tem topcs to be predcted. Generally, P@N and R@N are computed by the followng equatons: N P@ N N Nt R@ N N N t (7) t t Number of tems Number of ratngs Epnons 4, , ,824 Where N s the number of recommended tems, N s the t number of tems contaned n both the ground truth and the top-n results, N denotes the number of tems contaned n t the ground truth but not n the top-n results. Note that P@N and R@N are the averages of precson values and recall values, respectvely. We wll demonstrate how the performance of recommendaton models vares wth dfferent values of N n the (8) followng subsecton E. (2) Expermental Settng To nvestgate the capablty of the proposed PSO-RBF based recommendaton method, for both two datasets Epnons and Movelens, we random select 4%, 6% and 8% as the tranng set respectvely and the correspondng remanng 6%, 4% and 2% as the testng set. The random selecton process s carred out 1 tmes ndependently, and the average RMSE are obtaned. Note that parameters of the proposed PSO-RBF based recommendaton method are determned va cross valdaton. 3. Baselne methods We compare the proposed PSO-RBF method wth the three baselne recommendaton methods. Specfcally, the three baselne methods are presented as follows, (1) : refers to the user-based collaboratve flterng method and s a memory-based recommendaton method. manly adopts the neghborhood nformaton of users n the user-tem ratng matrx for supportng the whole recommendaton process. In ths paper, we use the cosne smlarty to calculate user-user smlarty. (2) : refers to the tem-based collaboratve flterng method and s also a memory-based recommendaton method. manly adopts the neghborhood nformaton of tems n the user-tem ratng matrx for supportng the whole recommendaton process. In ths paper, we use the Pearson correlaton to calculate tem-tem smlarty. (3) RBF neural network based method: RBF neural network s one of the neural networks. About the detaled nformaton of RBF neural network, please refer to the above mentoned ntroducton on RBF neural network. 4. Performance comparsons n terms of RMSE To answer the frst queston referred to n the begnnng of ths secton, we compare the proposed PSO- method wth the above three baselne methods on two real-world datasets. The comparatve results are shown n Table 3 on RMSE. Table 3. Performance comparson on Epnons and Movelens n terms of RMSE Epnons MoveLens Tranng sze PSO-RB F based 4% % % % % % From the comparatve results n Table 3, we can conclude the followng observatons: In general, under three dfferent tranng szes, method always outperforms method, and compared to other three baselne methods, the proposed PSO- method always obtans better performance than other three baselne methods and s more robust to the data sparsty problem. These results ndcate that RBF neural network optmzed by PSO algorthm can further ISSN:

5 mprove the recommendaton tem ratng predcton performance. Wth the ncreasng sze of the tranng set, we have enough expermental data to tran every model and every model becomes more stable, hence, the performances of the PSO- method and other three baselne methods are all mproved on RMSE to some extent. Va aforementoned analyss, we can draw an answer to the frst queston that RBF neural network optmzed by PSO algorthm not only can sgnfcantly mprove the recommendaton performance but also can mtgate the data sparsty problem n the recommendaton systems. In summary, the proposed PSO-RBF recommendaton method s superor to the smple collaboratve flterng method, because tranng from RBF neural network optmzed by PSO algorthm fltered out the ratng values wth greater evaluaton bas. 5. Performance comparsons n terms of P@N and R@N In order to further verfy the effectveness of utlzng PSO algorthm to optmze RBF neural network for evaluatng recommendaton lsts, through comparatve experments, P@N and R@N are utlzed to sort the predcted tems accordng to the ratngs and the values of N are set to 1, 15 and 2, respectvely. The comparatve expermental results are presented n Fg. 2, Fg. 3, Fg. 4 and Fg. 5 as follows. Fg. 3. R@N performance comparson on Epnons dataset (N=1, 15 and 2 respectvely) P@N Recommendaton lst sze Fg. 4. P@N performance comparson on Movelens dataset (N=1, 15 and 2 respectvely) PSO- PSO-.5 PSO- R@N P@N.3.1 Recommendaton lst sze Fg. 2. P@N performance comparson on Epnons dataset (N=1, 15 and 2 respectvely) R@N PSO- Recommendaton lst sze Recommendaton lst sze Fg. 5. R@N Performance comparson on Movelens dataset (N=1, 15 and 2 respectvely) We start wth dscussng the results showed n Fg. 2, Fg. 3, Fg. 4 and Fg. 5. The changes of all recommendaton models wth varyng recommendaton lst sze on Epnons and Movelens are presented. Obvously, for all models, larger recommendaton lst sze decreases the precson but ncreases the recall. That s to say, the larger the recommendaton lst sze, the lower the precson s and the hgher the recall s. Ths s because larger recommendaton lst sze ncreases the probablty that a user s tem rankng s ht by one of the recommended tems. The maxmum recommendaton lst sze s set to 2, because larger sze may affects the effectve nteracton between a user and the tem. In addton, when the value of N s fxed, our proposed PSO- recommendaton approach obvously outperforms, and method n terms of recall and precson. We carefully compare the values of P@N and R@N, for example, on Epnons dataset, the value of P@1 from the PSO- method s the largest compared wth method, method and method n Fg. 2. On Movelens dataset, the value of R@1 from the PSO-RBF based method s also the largest compared wth method, ISSN:

6 method and method n Fg. 5. And other comparsons about the values of P@N and R@N are also smlar. Ths s because predctng the mssng ratngs of tems makes the PSO- recommendaton method be able to accurately calculate the smlarty between tems, so as to mprove the accuracy of the recommendaton tem rankng predcton. Hence, we can conclude that the dea of compensaton values for tems mssng ratng can mprove the accuracy of the recommendaton lsts, and mtgates the data sparsty and cold-start problems n the tradtonal recommendaton systems to some extent. 6. Ablty of copng wth cold-start tems To answer the second queston mentoned n the begnnng of Secton 4, we nvestgate the capablty of the proposed PSO-RBF method n handlng cold-start problems. In detal, smlar wth the above dstrbuton of the tranng set and the test set, we randomly select 8% tems from the tranng set and remove ther tem ratngs from the tranng set to the test set. In ths way, these 8% of tems do not have any tem ratngs and we consder these 8% of tems as cold-start tems. The results on those tranng sets wth cold-start tems are lsted n Table 4 for RMSE. Table 4. Performance comparson on Epnons and Movelens wth 8% cold-start tems n terms of RMSE Epnons MoveLens Tranng sze 4% 6% 8% 4% 6% 8% RBF-based (-.2381) (-.2295) (-.182) (-.1794) (-.1539) (-.14) (-.199) (-.1986) (-.1971) (-.28) (-.1726) ( (-.2197) (-.2326) (-.1989) (-.1949) (-.1615) (-.1323) PSO-RBF based (-.179) (-.962) (-.848) (-.1162) (-.121) (-.983) Note that numbers nsde parentheses n Table 4 denote the performance reductons compared to the performance wthout cold-start users n Table 3. We analyze Table 4 and compare the performance wth Table 3 wthout cold-start tems, t can be concluded that the performance of all methods degenerates when we ntroduce cold-start tems. For example, when tranng sze s 8%, RMSE for PSO- method ncreases up to from n Table 3 on Epnons, that s to say, RMSE decreases by = RMSE for PSO- method ncreases up to from n Table 3 on Movelens, that s to say, RMSE decreases by = We carefully observe Table 3 and Table 4, the performance degeneraton of the proposed PSO- recommendaton method s much smaller compared to the other three baselne methods. These results support that the proposed PSO- recommendaton method can mtgate cold-start problems for the recommendaton. In summary, the ntroducton of cold-start tems could degrade the recommendaton performance and the proposed framework s relatvely more robust to cold-start tems by predctng the mssng ratngs of tems. IV. CONCLUSION RBF neural network optmzed by PSO algorthm s a regularzed network and can approach any contnuous functon, whch has a strong predctve power. In ths paper, RBF neural network optmzed by PSO algorthm s desgned to accurately predct the recommended tems ratngs, whch s dfferent from calculatng the user smlarty wth the same tems ratngs by users n the tradtonal user based recommendaton method. Compared wth,, and RBF-based method, the predctve result of the proposed PSO- method s more accurate. The comparatve results also show that the PSO- model has better stablty and s relatvely more robust to cold-start tems by predctng the mssng ratngs of tems than the exstng recommended tem ratng predcton. Our next research work wll ncorporate the smlarty of tems, the smlarty of users and the socal relatonshp among users nto the PSO- recommendaton model, and construct the correlatons among the smlarty of tems, the smlarty of users and the socal relatonshp among users whch further mprove the accuracy of the PSO- recommendaton model. REFERENCES [1] M. Deshpande, G. Karyps, Item-based Top-n Recommendaton Algorthms, ACM Transactons on Informaton Systems, vol. 22, no. 1, pp , 24 [2] Y. Koren, R. Bell, C. Volnsky, Matrx Factorzaton Technques for Recommender Systems, Computer, vol. 42, no. 8, pp. 3-37, 29 [3] J. Km, D. Lee, K. Chung, Item Recommendaton based on Context-aware Model for Personalzed U-healthcare Servce, Multmeda Tools and Applcatons, vol.71, no. 2, pp , 214 [4] K. Cho, Y. Suh, A New Smlarty Functon for Selectng Neghbors for Each Target Item n Collaboratve Flterng, Knowledge-Based System, vol. 37, no.1, pp , 213 [5] C. Cheng, H. Yang, I. Kng, M. R. Lyu, Fused Matrx Factorzaton wth Geographcal and Socal Influence n Locaton-based Socal Networks, Proceedngs of the Twenty-Sxth AAAI Conference on Artfcal Intellgence, pp , 212 [6] Y. Du, L. Huang, M. Ho, A Collaboratve Flterng Recommendaton Method Fuson Trust Computng, Pattern Recognton and Artfcal Intellgent, vol. 5, no. 1, pp , 214 [7] M. Duan, Collaboratve Flterng Recommendaton Algorthm based on Trust Propagaton, Internatonal Journal of Securty and Its Applcaton, vol. 9, no. 7, pp ,215 [8] C. Laa, D. Lub, C. Lnb, Novel Personal and Group-based Trust Models n Collaboratve Flterng for Document Recommendaton, Informaton Scence, pp.31-49, 213 [9] S. Rendle, L. S. Theme, Parwse Interacton Tensor Factorzaton for Personalzed Tag Recommendaton, Proceedngs of the thrd ACM Internatonal Conference on Web Search and Data Mnng, pp.81-9, 21 [1] J. Kennedy, R. C. Eberhart, Partcle Swarm Optmzaton, Proceedngs of IEEE Internatonal Conference on Neural Networks, pp , 1995 [11] S. Solomon, P. Thulasraman, R. Thulasram, Collaboratve Mult-swarm PSO for Task Matchng Usng Graphcs Processng Unts, Proceedngs of the 13th Annual Conference on Genetc and Evolutonary Computaton, pp , 211 ISSN:

7 [12] H. G. Han, Q. L. Chen, J. F. Qao, An Effcent Self-organzng RBF Neural Network for Water Qualty Predcton, Neural Networks, vol. 24, no. 7, pp , 211 [13] S. H. Wang, J. L. Tang, H. Lu, Toward Dual Roles of Users n Recommender Systems, Proceedngs of the 24th ACM Internatonal Conference on Informaton and Knowledge Manage., pp , 215 Chengfang Tan was born on Feb. 16, She receved the Master degree n computer scence and technology from Nanjng Normal Unversty of Chna. Currently, she s a researcher (assstant professor) at Suzhou Unversty, Chna. Her major research nterests nclude nformaton processng and data mnng. She has publshed many papers n related felds. Cayn Wang was born on Sept. 16, He receved the Master degree n computer scence and technology from Hefe Polytechnc Unversty, Chna.. Hs major research nterests nclude artfcal ntellgence. He has publshed many papers n related felds. Yuln L s an undergraduate student from the School of Computer Scence and Technology, Suzhou Unversty. Her research nterests nclude machne learnng and network congeston control. Xx Q s an undergraduate student from the School of Computer Scence and Technology, Suzhou Unversty. Her research nterests nclude computer networks and securty, machne learnng, and ntellgent nformaton processng. ISSN: