V2i1010 by IJCERT Publications

Volume 2, Issue 10, October-2015, pp. 847-852

ISSN (O): 2349-7084

International Journal of Computer Engineering In Research Trends Available online at: www.ijcert.org

Social Network Based "FndSearch” Recommender Framework 1

Telaprolu Swamulu, 2P.Sujatha

M.Tech (CSE), Department of Computer Science & Engineering, NRI Institute of Technology Associate Professor, Department of Computer Science & Engineering, NRI Institute of Technology 1

Swami9966@gmail.com ,2elesuja@gmail.com

Abstract: - Social networks give an essential origin of data with respect to clients and their cooperation’s which is exceptionally important for the recommender systems. In web-based interpersonal organizations, social trust connections between clients demonstrate the likeness of their needs and assessments. In this paper, we introduced a Social network based recommender framework named "FndSearch" an application that uses the data of the client and makes suggestions by considering client's real intrigue and figuring the likenesses between every client, consequently prescribing companions. A probabilistic model is being created to make this customized proposal from the fundamental data gathered from the client. We additionally help the clients in a manner via looking and prescribing companions who don't have a place with the same classification of the significant enthusiasm as the client.

Keywords: - Social networks, Recommender system, user interest, personalized recommendation.

——————————  ——————————

I. INTRODUCTION

Social networking sites have a massive information set of clients, as indicated by the present study. Each individual informal communication site makes the record of the exercises of clients, for example, his/her enjoys; what client likes? , what client is doing? , what is client's interest? And so on and it has picked up the primary territory of center in comprehension the client conduct, One of the best cases we should seriously mull over his Face Book. Consequently here, in our methodology we are making utilization of client way of life as the real sympathy toward suggesting companions and manufacture relationship among the general population with comparative intrigue and share data or fabricate correspondence among likely minded individuals. With Diagram Programming interface gave by Face Book engineers, we separate client interest which has a tendency to find the way of life of the client. The settings might likewise incorporate data like the theme of intrigues, side interests, calling, and so on the

data about the client interest and his/her calling can be utilized to prescribe companions. Person to person communication is making so as to extend the quantity of social contacts associations through people. While informal communication has gone on very nearly the length of social orders themselves have existed, the unparalleled capability of the web to advance such associations is just now being completely perceived and abused, through Electronic gatherings set up for that reason. it sets up interconnected Web groups that individuals make contacts that would be beneficial for them to know. Sites committed to informal communication incorporate Linkedin, Face book and so forth. Fig. 1. Interpersonal interaction Informal communities are currently gone to more regularly than individual email is perused. Some informal organizations have developed to such huge extents that they match whole nations as far as populace If Facebook, for instance, was a nation it would be the fifth-most populated on the planet. Facebook is one among the most mainstream destinations in this time of

Page | 847

Swamulu et al., International Journal of Computer Engineering In Research Trends Volume 2, Issue 10, October-2015, pp. 847-852 correspondence and sharing. As per the insights, it has 1.4 billion dynamic clients! Recommender systems (RS) are the ones used to prescribe companions to the clients based on some criteria. They are typically used to handle and comprehend the data over-burden. The current interpersonal interaction framework incorporates Netflix for a film suggestion, Facebook for friend proposal, foursquare to recommend places and so on. The test for the current long range informal communication administrations is the means by which to prescribe a decent friend to a client as the vast majority of them depend on the previous client connections to pick the companions. There are ordinarily two sorts of calculations for suggested systems1) substance based routines measure the closeness of the prescribed item(target thing) to the ones that an objective client likes or abhorrence’s based on the thing attributes[10]. 2) Community oriented separating system that discovers clients with tastes that are like the objective clients based on their past evaluations. This can then make the suggestions to the objective client based on the feelings of the comparable clients [11]. For over 10 years, RS's have been proposed to defeat the data over-burden and numerous calculations and systems have been produced for the same. Still, they confront the test of cool begin clients and information sparsely on account of Communitarian shifting strategy. The late rise of online interpersonal organizations gives us a tremendous measure of data identified with client conduct and friend cooperation’s have exhibited their significance to building up an effective RS in this field. Facebook's friend recommender framework is based on the idea of social diagrams. It components individuals as the general population you may know through associations on the clients profile i.e. shared companions based on their work, instruction points of interest, systems and so on. For example, if both two clients A and B include the same school in their instruction, then both A and B will be appeared with the general population they may know on one another's profile or if both A and B have huge number of shared companions then Facebook surmises that A and B may know one another thus the suggestion takes after. This framework wasn't observed to be much suitable.

Fig 1. Social Network In this paper, we have exhibited an application named "FndSearch" that concentrates the clients essential data like name, id, intrigues, they have preferred on from the Facebook database and stores this data into our backend database. Once the client logins through the application, naturally his fundamental points of interest will be gathered and put away and the significant enthusiasm of every client will be ascertained through likelihood and spared. At that point, the similitude’s between every client are likewise figured and after that prescribe companions based on the most elevated likenesses between the clients. The client can likewise seek companions other than his real enthusiasm from the arrangement of individuals who are available in our database.

II. EXISTING SYSTEM Online social networks have ended up imperative center points of social movement and channels of data. Well known social networking locales, for example, FaceBook, the social news aggregator, and the micro blogging administration Twitter have experienced unstable development. Despite the fact that FaceBook has the methodology of the prescribing friend which is based on a common friend that makes utilization of friend of friend methodology observed to be not that proper.

Page | 848

Swamulu et al., International Journal of Computer Engineering In Research Trends Volume 2, Issue 10, October-2015, pp. 847-852 subjects in the client record utilizing LDA model that is considering the probabilistic system to discover overwhelming way of life vector and afterward prescribing to the question client with potential friend whose qualities are more noteworthy than certain predetermined limit esteem.

III. PROPOSED SYSTEM

Figure 2.social graph constructed by 8 users Hence, this persuaded to produce the structure of suggesting the friend with the comparative hobby. With the quantities of dynamic clients on these locales numbering in the millions or even several million, recognizing individuals with comparable enthusiasm among them turns into a critical issue with application in advertising, data scattering, hunt, and mastery revelation. Recommender Systems are programming apparatuses and procedures giving proposals to things to be useful to a client [5,6]. In the current framework, numerous prescribing systems have their own proposed structure for doling out positions to the client exercises and having a different customized suggestion. For example, Netflix for a film suggestion, Foursquare to recommend places, FaceBook for prescribing friend based on common companions. In which prescribing friend based on common companions is not that proper, these are the different inconveniences that spurred us to propose another framework. In this paper, we considered FaceBook for removing the client subtle elements, for example, name, interest, email id and so forth and we have dissected its structure. From our study viewpoint, one of the vital elements of this system is client interest. Client hobby is the procedure by which considerations and activities of individual are created and delineated in their profile and can examine on it to distinguish his/her way of life. This can be generally acknowledged in social networks. Henceforth, the paper goes for satisfying the improvement of the accompanying framework: Considering, FaceBook profile information, we figure probabilities of the

In this paper, we have introduced another application named "FndSearch". This is an online application that is specifically being coordinated with the Facebook(Fb) database. The client who logs through the application, with substantial Fb certifications will be coordinated to the landing page of the application. The pictorial representation is as appeared underneath in Fig.2. In our undertaking, we have considered just five classes for the computation of the significant hobby i.e. Music, Motion pictures, Games, Recreations, and Books. The framework of our system consists of two main parts: 1) Web application - "FndSearch" is the web application that we have created here for the working in the customer side with HTML and PHP stages. Once the application was created, we gave a connection to the clients through which they could sign the application through legitimate Fb qualifications. A session will start and a solicitation will be sent to the server to get the data of the client. At the point when the client permits the entrance consent, an entrance token will be produced for every client for validation, and in this way the client information will be recovered. This is the real action of the web application. 2) Calculations and recommendations - Once the client is signed in through our application, the client will be coordinated to the appreciated page of the application. There, "Find companions" alternative will trigger our calculations of discovering the probabilities of every client's real hobby. At that point, we discover the likenesses between every one of the clients who are signed into the database. Subsequent to getting the similitude, based on the qualities acquired for the comparability, the client who's signed in will be prescribed with companions. At the point when the client taps on the "Inquiry" alternative, the client information is removed from the database and each of their preferences in each class is being contrasted with locating their significant hobby. These qualities are put away independently and after that the client will be suggested based on the class of interest the client taps

Page | 849

Swamulu et al., International Journal of Computer Engineering In Research Trends Volume 2, Issue 10, October-2015, pp. 847-852 on. On the off chance that the significant enthusiasm of a man is more than in one class, then that individual additionally will be prescribed to the client. Since our application is straightforwardly incorporated with the Fb database, once the client sign in, his/her fundamental data like name, profile pic, preferences, mail id, area and so on will be gathered and put away into our outer database. This data will be available in the Facebook SDK's Diagram Programming interface device and can be extricated if the client gives the entrance consents. During the web development phase, the user data is recorded into our database. The user activity from the database is accessed. An algorithm for calculating dominating life style vector of user is developed. LDA algorithm is a way of automatically discovering topics that the sentences in document has, it finds the topic by calculating the probability of words in document. Similarly in case of FaceBook we apply this method and find the dominant life style vector as below, . The life style of users is extracted by the life style analysis module with the probabilistic topic model, and then the life style indexing module puts the life styles of users into the database in the format of (life-style, user) The probabilistic topic model can be given as, P(Wi |dk)=∑Z j=1 p(wi |zj) p(zj |dk) Where, w-activity Z-life style D –set of document and in our case as we are implementing it in facebook, dk can be considered as 1 as we are able to fetch the topics directly by considering user activity as whole document. The topics may be movies, books read, sports etc. and the count of these activities can be accessed based on the permission given by FaceBook developers and the people who logs in to our app and allow us to access the data to recommend them friend of similar interest among the people from our database. As we get the count values we calculate probability of each activity of user using above formula then we find dominating life style vector of user by specifying some assumed threshold value in our case we have considered it as 0.2.let us define this threshold as (alpha) And after finding dominating life style vector of user we find similarity between the users this is done using the below formula, S=Sc(i,j).Sd(i,j). Where i & j are number of users Sc=is cosine similarity and Sd= is distance similarity. Hence, a cosine similarity can be calculated as below Between user 1 & user 2, Sc(U1,U2)=COS(U1,U2)=a.b/|a||b| Similarly

with all the users it is calculated. And distance Similarity is calculated as below, Sd(U1,U2)=2|D1∩D2|/|D1|+|D2|. After calculating similarity value for all the user with every Other user we store those values in matrix form from which We recommend a friend to the user who is greater than Some specified threshold value, we have assumed Threshold value as 0.5 in our case and let’s consider this Recommending threshold as β(beta). In the proposed work, we have focused on four important phases as below (Fig.2):

a. Creating a user interface application for login: Web applications that oblige approval to get to certain data. Your login page checks a client's name and secret word, puts a treat on the client's PC so he can return later, and utilizes database inquiries to recover the individual data for the client.

b. Extracting user data and storing in database: We utilize Chart Programming interface devices for separating information. The benefits of Chart Programming interface over past work are the capacity to learn profoundly exact extraction guidelines, and afterward we store this client data like 'name', 'email', likes, in the database that we have made. c. Finding dominant life style: Contingent upon the exercises that client has done we get the sure number of the action, then we figure probabilities of every way of life and consider those qualities who are more noteworthy than some predefined limit esteem α (alpha). In which the client communicates with the site through our application. d. Prescribing potential companion: We ascertain the comparability between the clients and prescribe companions to the inquiry clients who are over sure edge esteem β (beta)

Fig3. Basic model

IV.PROPOSED WORK Dependencies of our framework User must be loggedin into application connected to the Face book. All the user activities are tracked and dumped into database

Page | 850

Swamulu et al., International Journal of Computer Engineering In Research Trends Volume 2, Issue 10, October-2015, pp. 847-852 along with user access permissions. Based on the information collected in the database potential friend is being recommended to the query user. Main steps to find people with similar interest and recommend querying user, the main steps are as follows: Develop a web application which connects to Facebook Login page through which we can host the app and users can give permissions. To retrieve the user data through the access tokens specified for each user. Based on accessing permission given by user for a web application we can get the activities performed by user. Develop and test a methodology to find the users with similar interest in online social networks on the basis of a simple metric of their activity level. We calculate the probabilistic values of each activity and find dominating life style, and recommend potential friend to the query user.

liked, etc. And it is stored into the database then this is used for recommending friends. Fig.4.Architectural model of proposed system

4.1. EVALUATION A. Database Design having THREE main parts: 1. First we have to store the user email_id , movies liked, books liked and give unique serial id to users as shown in Table.1. 2. Secondly, we collect all user permissions and group them as different attributes. 3. Next when we collect the entire user permissions those should also be stored in database as integer values because we are considering the count of user activities. Table.1 shows the sample users taken for conducting the experiment. It shows the data which has been taken from database, it includes the user email_ID, and the activities done (Count) when logged into their FB account.Both of large-scale simulations and small-scale experiments are conducted to evaluate the performance of Friendbook. In real experiments, eight volunteers of different professions help to contribute their data to evaluate Friend book. Table 1 User likes

Fig.4.Architectural model of proposed system Fig.4. shows the architectural diagram for the proposed problem; here we can see the flow of various actions that has been shown in the block diagram. The first step is log-in to the web page checks for the correct id and password and does verification. Once it is correct it is moved on to the webpage and it collects all the information such as name, email, and the activities performed by the user such as movies watched, sports

Page | 851

Swamulu et al., International Journal of Computer Engineering In Research Trends Volume 2, Issue 10, October-2015, pp. 847-852

In our methodology, we exhibited the design and usage of FndSearch, a semantic-based friend proposal framework for social networks. Not the same as the friend proposal instruments depending on social charts in existing social networking administrations, the outcomes demonstrated that the suggestions precisely mirror the inclinations of clients in picking companions. Past the present model, the future work can be focused on actualizing it on other social networking, and same can be utilized to manufacture a stand-alone application and access the client movement through versatile sensors. FndSearch can use more data forever disclosure, which ought to enhance the suggestion involvement later on.

[8] K. Farrahi and D. Gatica-Perez. Probabilistic mining of sociogeographic routines from mobile phone data. Selected Topics in Signal Processing, IEEE Journal of, 4(4):746-755, 2010. [9] K. Farrahi and D. Gatica-Perez. Discovering Routines from Largescale Human Locations using Probabilistic Topic Models. ACM Transactions on Intelligent Systems and Technology (TIST), 2(1), 2011. [10] L. Gou, F. You, J. Guo, L. Wu, and X. L. Zhang. Sfviz: Interestbased friends exploration and recommendation in social networks. Proc. of VINCI, page 15, 2011.

REFERENCES [1] Prem Melville and Vikas Sindhwani, Recommendation Systems, In Encyclopedia of Machine Learning, Claude Sammut and Geoffrey Webb (Eds), Springer, 2010 Chapter No: 00338, Pg 829-838 [2] W. H. Hsu, A. King, M. Paradesi, T. Pydimarri, and T. Weninger. Collaborative and structural recommendation of friends using weblog-based social network analysis. Proc. Of AAAI Spring Symposium Series, 2006. [3] Zhibo Wang, Jilong Liao, Qing Cao, Hairong Qi, and Zhi Wang, “Friendbook: A Semantic-based Friend Recommendation System for Social Networks”IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 99, MAY2014 [4] J. Kwon and S. Kim. Friend recommendation method using physical and social context. International Journal of Computer Science and Network Security, 10(11):116-120, 2010. [5] A.Kirankumar, P.Ganesh Kumar Reddy, A.Ram Charan Reddy” A Logic-based Friend Reference Semantic System for an online Social Networks”, International Journal Of Computer Engineering In Research Trends Volume 1, Issue 6, December 2014, PP 501-506,www.ijcert.org. [6] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3:993- 1022, 2003. [7] L. Bian and H. Holtzman. Online friend recommendation through personality matching and collaborative filtering. Proc. of UBICOMM, pages 230235, 2011.

Page | 852