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  • 1. R.Priyadharshini, N.Geethanjali, B. Dhivya / International Journal of Engineering Researchand Applications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.238-242238 | P a g eMining and Predicting Users M-Commerce Patterns usingCollaborative Filtering AlgorithmR.Priyadharshini1, N.Geethanjali2, B. Dhivya3PG ScholarsDepartment of Information Technology SNS College of Technology, Coimbatore, IndiaAbstractMobile Commerce, also known as M-Commerce or mCommerce, is the ability toconduct commerce using a mobile device.Research is done by Mining and Prediction ofMobile Users’ Commerce Behaviors such astheir movements and purchase transactions. Theproblem of PMCP-Mine algorithm has beenovercome by the Collaborative FilteringAlgorithm. The main objective is to analyse theMobile users’ movements to the new locationsinstead of considering only the frequent movinglocations. In the existing approach, a MobileCommerce Explorer Framework has beenimplemented to make recommendations forstores and items by analysing the Mobile users’.The drawbacks are the recommendations thatmade are only for frequently moving locationsand stores. The proposed work is to recommendstores and items in new locations by consideringthe rating of items given by the other users innew locations.Keywords– Mining, Prediction, MobileCommerce.I. INTRODUCTIONWith the rapid advance of wirelesscommunication technology and the increasingpopularity of powerful portable devices, mobileusers not only can access worldwide informationfrom anywhere at any time but also use theirmobile devices to make business transactionseasily, e.g., via digital wallet [1]. Meanwhile, theavailability of location acquisition technology, e.g.,Global Positioning System (GPS), facilitates easyacquisition of a moving trajectory, which records auser movement history. At developing patternmining and prediction techniques that explore thecorrelation between the moving behaviors andpurchasing transactions of mobile users to explorepotential M-Commerce features. Owing to therapid development of the web 2.0 technology,many stores have made their store information,e.g., business hours, location, and features availableonline.Collecting and analysing user trajectoriesfrom GPS-enabled devices. When a user enters abuilding, the user may lose the satellite signal untilreturning outdoors. By matching user trajectorieswith store location information, a users’ movingsequence among stores in some shop areas can beextracted. The mobile transaction sequencegenerated by the user is {(A,{i1}), (B,Ø), (C,{i3}),(D,{i2}), (E,Ø),(F,{i3,i4}), (I,Ø),(K,{i5})}. There isan entangling relation between moving patterns andpurchase patterns since mobile users are movingbetween stores to shop for desired items. Themoving and purchase patterns of a user can becaptured together as mobile commerce patterns formobile users. To provide this mobile ad hocadvertisement, mining mobile commerce patternsof users and accurately predicts their potentialmobile commerce behaviors obviously are essentialoperations that require more research.Fig 1 Example of Mobile Transaction SequenceTo capture and obtain a betterunderstanding of mobile users’ mobile commercebehaviors, data mining has been widely used fordiscovering valuable information from complexdata sets. They do not reflect the personalbehaviors of individual users to support M-Commerce services at a personalized level. MobileCommerce or M-Commerce, is about the explosionof applications and services that are becomingaccessible from Internet-enabled mobile devices. Itinvolves new technologies, services and businessmodels. It is quite different from traditional e-Commerce. Mobile phones impose very differentconstraints than desktop computers.II. PROBLEM DEFINITIONIn the MCE framework, frequentlymoving locations and frequently purchased itemsare considered for analysing mobile users’commerce behavior. The Personal Mobile
  • 2. R.Priyadharshini, N.Geethanjali, B. Dhivya / International Journal of Engineering Researchand Applications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.238-242239 | P a g eCommerce Pattern-Mine (PMCP-Mine) algorithmwas used to find only frequent datasets, by deletingin-frequent data in the Mobile Commerce ExplorerDatabase. Also, recommendations were done onlyfor the frequent datasets. The similarity values thatwere found in the Similarity Inference Model(SIM) were not accurate.III. LITERATURE SURVEYChan Lu, Lee and S. Tseng developed theMobile Commerce Explorer Framework for miningand prediction of mobile users’ movements andpurchases [1]. Agrawal and Swami presented anefficient algorithm [2] that generates all significantassociation rules between items in the database.Han, Pei and Yin proposed a novelfrequent-pattern tree (FP-tree) structure, which isan extended prefix-tree [3] structure for storingcompressed, crucial information about frequentpatterns, and develop an efficient FP-tree basedmining method, FP-growth, for mining thecomplete set of frequent patterns by patternfragment growth.Herlocker, Konstan, Brochers and Riedldeveloped an Automated Collaborative Filtering isquickly becoming a popular technique for reducinginformation overload, often as a technique tocomplement content-based information filteringsystems [8]. In this paper, present an algorithmicframework for performing Collaborative Filteringand new algorithmic elements that increase theaccuracy of Collaborative Prediction algorithms.Then present a set of recommendations on selectionof the right Collaborative Filtering algorithmiccomponents.IV. EXISTING SYSTEMA novel framework for the mobile users’commerce behaviors has been implemented formining and prediction of mobile users’. MCEframework has been implemented with threecomponents: 1) Similarity Inference Model (SIM)for measuring the similarities among stores anditems, 2) Personal Mobile Commerce Pattern Mine(PMCP-Mine) algorithm for efficient discovery ofmobile users’ Personal Mobile Commerce Patterns(PMCPs), 3) Mobile Commerce Behavior Predictor(MCBP) for prediction of possible mobile userbehaviors. In the MCE framework, only frequentlymoved locations and frequently purchased itemsare considered. The modules proposed inframework are:A. Mobile Network DatabaseThe mobile network database maintainsdetailed store information which includes locations.B. Mobile User Data BaseThe Mobile User database maintainsdetailed mobile user information which includenetwork provider.C. Applying Data Mining MechanismSystem has an “offline” mechanism for Similarityinference and PMCPs mining, and an “online”engine for mobile commerce behavior prediction.When mobile users move between the stores, themobile information which includes useridentification, stores, and item purchased are storedin the mobile transaction database. In the offlinedata mining mechanism, develop the SIM modeland the PMCP Mine algorithm to discover thestore/item similarities and the PMCPs, respectively.Similarity Inference Model for measuring thesimilarities among stores and items. PersonalMobile Commerce Pattern-Mine (PMCP-Mine)algorithm is used for efficient discovery of mobileusers’ Personal Mobile Commerce Patterns.D. Behavior prediction engineIn the online prediction engine,implemented a MCBP (Mobile CommerceBehavior Predictor) based on the store and itemsimilarities as well as the mined PMCPs. When amobile user moves and purchases items among thestores, the next steps will be predicted according tothe mobile user’s identification and recent mobiletransactions. The framework is to support theprediction of next movement and transaction.Mobile Commerce Behavior Predictor forprediction of possible mobile user behaviors.E. Similarity Inference ModelA parameter-less data mining model,named Similarity Inference Model, to tackle thistask of computing store and item similarities.Before computing the SIM, derive two databases,namely, SID and ISD, from the mobile transactiondatabase. An entry SIDpq in database SIDrepresents that a user has purchased item q in storep, while an entry ISDxy in database ISD representsthat a user has purchased item x in store y.Deriving the SIM to capture the similarity scorebetween stores/items. For every pair of stores oritems, SIM assigns them a similarity score. In SIM,used two different inference heuristics for thesimilarity of stores and items because some stores,such as supermarkets, may provide various types ofitems.By applying the same similarity inferenceheuristics to both of stores and items, various typesof items may be seen as similar since differentsupermarkets are seen as similar. Based on ourheuristics, if two stores provide many similar items,the stores are likely to be similar; if two items aresold by many dissimilar stores, the stores areunlikely to be similar. Since the store similarity anditem similarity are interdependent, computing thosevalues iteratively. For the store similarity, considerthat two stores are more similar if their provideditems are more similar. Given two stores sp and sq,compute their similarity SIM (sp; sq) by calculatingthe average similarity of item sets provided by spand sq. For every item sold in sp (and, respectively,sq), first find the most similar item sold in sq (and,
  • 3. R.Priyadharshini, N.Geethanjali, B. Dhivya / International Journal of Engineering Researchand Applications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.238-242240 | P a g erespectively, sp). Then, the store similarity can beobtained by averaging all similar item pairs.Therefore, SIM (sp; sq) is defined assim (sp,sq)= ∑φϵГsp MaxSim(φ,Гsq)+∑ϒϵГsq MaxSim(ϒ, Гsp)|Гsp|+|Гsq|Where MaxSim(e,E) = Maxe’ϵE sim(e,e’) representsthe maximal similarity between e and the elementin E. Гsp and Гsq are the sets of items sold in sp andsq, respectively. On the other hand, for the itemsimilarity, consider that two items are less similar ifthe items are sold by many dissimilar stores. Giventwo items ix and iy, compute the similarity sim(ix,iy)by calculating the average dissimilarity of store setsthat provide ix and iy. For every store providing ix(and, respectively, iy), first find similarity byaveraging all dissimilar store pairs.F. Personal Mobile Commerce Pattern-MineAlgorithmThe PMCP-Mine algorithm is divided intothree main phases: 1) Frequent-TransactionMining: A Frequent- Transaction is a pair of storeand items indicating frequently made purchasingtransactions. In this phase, first discover allFrequent-Transactions for each user. 2) MobileTransaction Database Transformation: Based onthe all Frequent-Transactions, the original mobiletransaction database can be reduced by deletinginfrequent items. The main purpose is to increasethe database scan efficiency for pattern supportcounting. 3) PMCP Mining: This phase is miningall patterns of length k from patterns of length k-1in a bottom-up fashion.G. Mobile Commerce Behavior PredictorMCBP measures the similarity score ofevery PMCP with a user’s recent mobile commercebehavior by taking store and item similarities intoaccount. In MCBP, three ideas are considered: 1)the premises of PMCPs with high similarity to theuser’s recent mobile commerce behavior areconsidered as prediction knowledge; 2) more recentmobile commerce behaviors potentially have agreater effect on next mobile commerce behaviorpredictions and 3) PMCPs with higher supportprovide greater confidence for predicting users’next mobile commerce behavior. Based on theabove ideas, propose a weighted scoring function toevaluate the scores of PMCPs. For all PMCPs,calculate their pattern score by the weightedscoring function. The consequence of PMCP withthe highest score is used to predict the next mobilecommerce behavior.H. Performance ComparisonConduct a series of experiments toevaluate the performance of the proposedframework MCE and its three components, i.e.,SIM, PMCP-Mine, and MCBP under varioussystem conditions. The experimental results showthat the framework MCE achieves a very highprecision in mobile commerce behaviorpredictions. Besides, the prediction techniqueMCBP in our MCE framework integrates themined PMCPs and the similarity information fromSIM to achieve superior performs in terms ofprecision, recall, and F-measure. The experimentalresults show that the proposed framework and threecomponents are highly accurate under variousconditions.Fig 2 Performance ComparisonV. PROPOSED SYSTEMA. Similarity Inference ModelPropose a parameter-less data miningmodel, named Similarity Inference Model, to tacklethis task of computing store and item similarities.Before computing the SIM, derive two databases,namely, SID and ISD, from the mobile transactiondatabase. An entry SIDpq in database SIDrepresents that a user has purchased item q in storep, while an entry ISDxy in database ISD representsthat a user has purchased item x in store y.Deriving the SIM to capture the similarity scorebetween stores/items. For every pair of stores oritems, SIM assigns them a similarity score. In SIM,used two different inference heuristics for thesimilarity of stores and items because some stores,such as supermarkets, may provide various types ofitems.By applying the same similarity inferenceheuristics to both of stores and items, various typesof items may be seen as similar since differentsupermarkets are seen as similar. Based on ourheuristics, if two stores provide many similar items,the stores are likely to be similar; if two items aresold by many dissimilar stores, the stores areunlikely to be similar. Since the store similarity anditem similarity are interdependent, computing thosevalues iteratively. For the store similarity, consider
  • 4. R.Priyadharshini, N.Geethanjali, B. Dhivya / International Journal of Engineering Researchand Applications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.238-242241 | P a g ethat two stores are more similar if their provideditems are more similar. Given two stores sp and sq,compute their similarity SIM (sp; sq) by calculatingthe average similarity of item sets provided by spand sq. For every item sold in sp (and, respectively,sq), first find the most similar item sold in sq (and,respectively, sp). Then, the store similarity can beobtained by averaging all similar item pairs.Fig 3 Block Diagram of Mobile CommerceExplorer FrameworkB. Collaborative Personal Mobile CommercePattern AlgorithmThe proposed system is developed byimplementing CPCMP - Collaborative PCMPAlgorithm which takes into account the newlyupdated locations and predicts behavior of the userbased on the Collaborative Filtering. Althoughcollaborative filtering methods have beenextensively studied recently, most of these methodsrequire the user-item rating matrix. However, onMCE Database, in most of the cases, other userpreferences and transactions are not alwaysavailable. Hence, collaborative filtering algorithmscannot be directly applied to most of therecommendation tasks on the database, like querysuggestion etc.Combine the Collaborative filteringaspects of predicting the unknown entities alongwith the proposed PCMP which mining the patternsof the user transactions behavior. This hybridalgorithm facilitates dynamic predictions and hencerecommendation to the users for better customerservice and experience. For better similarityinference modelling in cases of users visitingunknown locations, a new hybrid similarityinference model is proposed to take into accountthe items transacted and stores visited by a similaruser in the same location who have similar Personalmobile commerce pattern – i.e the frequent miningpatterns of the unknown user matches with respectto the user under consideration. So, the proposedwork improves the quality of predictions of thepreferred items and stores of the customer or userthereby bring about better sales and customersupport experience and feedback.C. Mobile Commerce Behavior PredictorPropose MCBP, which measures thesimilarity score of every PMCP with a user’s recentmobile commerce behavior by taking store anditem similarities into account. In MCBP, threeideas are considered: 1) the premises of PMCPswith high similarity to the user’s recent mobilecommerce behavior are considered as predictionknowledge; 2) more recent mobile commercebehaviors potentially have a greater effect on nextmobile commerce behavior predictions and 3)PMCPs with higher support provide greaterconfidence for predicting users’ next mobilecommerce behavior. Based on the above ideas,propose a weighted scoring function to evaluate thescores of PMCPs.VI. CONCLUSIONA novel framework namely MCE wasproposed for mining and prediction of mobileusers’ movements and transactions in mobilecommerce environments. In the MCE frameworkwere designed with three major techniques: 1) SIMfor measuring the similarities among stores anditems; 2) PMCP-Mine algorithm for efficientlydiscovering mobile users’ PMCPs; and 3) MCBPfor predicting possible mobile user behaviors. Tobest knowledge, it is the first work that facilitatesmining and prediction of personal mobilecommerce behaviors that may recommend storesand items previously unknown to a user. Toevaluate the performance of the proposedframework and three proposed techniques,conducted a series of experiments.The experimental results show that theframework MCE achieves a very high precision inmobile commerce behavior predictions. Besides,the prediction technique MCBP in MCE frameworkintegrates the mined PMCPs and the similarityinformation from SIM to achieve superior performsin terms of precision, recall, and F-measure. Theexperimental results show that the proposedframework and three components are highlyaccurate under various conditions.To overcome the problems of user movingto new locality, Collaborative Filtering algorithmwas implemented to recommend the users about thestores and items instead of considering onlyfrequent data.VII.FUTURE ENHANCEMENTFor the future work, we plan to exploremore efficient mobile commerce pattern mining
  • 5. R.Priyadharshini, N.Geethanjali, B. Dhivya / International Journal of Engineering Researchand Applications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.238-242242 | P a g ealgorithm, design more efficient similarityinference models, and develop profound predictionstrategies to further enhance the MCE framework.In addition, we plan to apply the MCE frameworkto other applications, such as object tracking sensornetworks and location based services, aiming toachieve high precision in predicting objectbehaviors.REFERENCES[1] Eric Hsueh-Chan Lu, Wang-Chien Lee,and Vincent S. Tseng, “A Framework forPersonal Mobile Commerce PatternMining and Prediction” IEEE transactionson knowledge and data engineering year2012.[2] R. Agrawal, T. Imielinski, and A. Swami,“Mining Association Rule between Sets ofItems in Large Databases,” Proc. ACMSIGMOD Conf. Management of Data, pp.207–216, May 1993.[3] J. Han, J. Pei, and Y. Yin, “MiningFrequent Patterns without CandidateGeneration,” Proc. ACM SIGMOD Conf.Management of Data, pp. 1-12, May 2000.[4] S.C. Lee, J. Paik, J. Ok, I. Song, and U.M.Kim, “Efficient Mining of User Behaviorsby Temporal Mobile Access Patterns,”Int’l J. Computer Science Security, vol. 7,no. 2, pp. 285-291, Feb. 2007.[5] V.S. Tseng and K.W. Lin, “EfficientMining and Prediction of User BehaviorPatterns in Mobile Web Systems”,Information and Software Technology,vol. 48, no. 6, pp. 357-369, June 2006.[6] X. Yin, J. Han, P.S. Yu, “LinkClus:Efficient Clustering via HeterogeneousSemantic Links,” Proc. Int’l Conf. VeryLarge Data Bases, pp. 427-438, Aug.2006.[7] C.H. Yun and M.S. Chen, “Mining MobileSequential Patterns in a MobileCommerce Environment,” IEEE Trans.Systems, Man, and Cybernetics, Part C,vol. 37, no. 2, pp. 278-295, Mar. 2007.[8] J.L. Herlocker, J.A. Konstan, A. Brochers,and J. Riedl, “An Algorithm Frameworkfor Performing Collaborative Filtering,”Proc. Int’l ACM SIGIR Conf. Researchand Development in InformationRetrieval, pp. 230-237, Aug 1999.[9] J. Han and Y. Fu, “Discovery of Multiple-Level Association Rules in LargeDatabase,” Proc. Int’l Conf. Very LargeData Bases, pp. 420-431, Sept. 1995.[10] Y. Zheng, L. Zhang, X. Xie, and W.Y.Ma, “Mining Interesting Location andTravel Sequences from GPS Trajectories,”Proc. Int’l World Wide Web Conf., pp.791-800, Apr. 2009.[11] Y. Tao, C. Faloutsos, D. Papadias, and B.Liu, “Prediction and Indexing of MovingObjects with Unknown Motion Patterns,”Proc. ACM SIGMOD Conf. Managementof Data, pp. 611-622, June 2004.