Users Activity based Recommendation Systems and Efficient Data Sharing in Social Networking Services by IJIRST

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 02 | July 2016 ISSN (online): 2349-6010

Users Activity based Recommendation Systems and Efficient Data Sharing in Social Networking Services Lighitha K R M. Tech. Student Department of Computer Science & Engineering NCERC Thrissur

Silja Varghese Assistant Professor Department of Computer Science & Engineering NCERC Thrissur

Blessy Joy Assistant Professor Department of Computer Science & Engineering NCERC Thrissur

Abstract Online social networking sites like, Facebook, Google and Twitter are suggested to share their public and personal information and make social relationship or connection with individual or people who can be even strangers. Existing social networking facilities recommends friends to users based on their social graphs, which might not be suitable to reflect a user’s preferences on friend selection in their real life. In this system, human interest based friend recommendation system for social networks, which recommends friends to users based on his/her life styles instead of their social graphs and determine life styles of users from user-centric sensor data and measures the comparison of life styles between users and this scheme recommends friends to users if their way of lifestyle has high similarity. Social networking sites also include sharing of files or data among the users or group of users. Data sharing is not easier and an accurate analysis on the shared data provides more benefits to both the society and individuals. Data sharing with a large number of participants must take into account many issues, that is efficiency, data integrity and privacy of data owner. Also ranking is done based on searching of users profile information. Finally, this system also take part a feedback mechanism to improve the users satisfaction and recommendation accuracy. Keywords: Activity Recognition, Lifestyle Modeling, Recommendation System, SNS _______________________________________________________________________________________________________ I.

INTRODUCTION

Twenty years ago, people typically made friends with others who live or work close to themselves, such as neighbors or colleagues. We call friends made through this traditional fashion as G-friends, which stands for geographical location-based friends because they are inﬂuenced by the geographical distances between each other. With the rapid advances in social networks, services such as Facebook, Twitter and Google+ have provided us revolutionary ways of making friends. According to Facebook statistics, a user has an average of 130 friends, perhaps larger than any other time in history. One challenge with existing social networking services is how to recommend a good friend to a user. Most of them rely on pre-existing user relationships to pick friend candidates. For example, Facebook relies on a social link analysis among those who already share common friends and recommends symmetrical users as potential friends. Unfortunately, this approach may not be the most appropriate based on recent sociology ﬁndings. The main areas of research in this field are: Social Networking Sites A social networking service or site(SNS) is a large dais to build social networks or relationship among individuals who share their related interests, surroundings of users or real-life connections. Social networking sites are web based services that allow individuals or users to generate a public profile, creating a list of users with whom to share their connections. Social network have become an unlimited resource of knowledge or information, for that there are several applications have been proposed to mine the information from social networks such as recommender system. The expansion of the social networks from the Internet created a major improvement in knowledge growth. From facts to search and then search to social interaction, users around the world are now greatly involved with the Internet as user generated content undertakes perceptual growth and expansion. Today’s social networking services are popular means to make contact or exchange ideas with people or persons around the world. These social networking sites behave like an online circle. Many of these online community members share their common classes in hobbies, tastes and other lifestyles. Some of the similar and popular social networking services are Facebook, Twitter, Google, Instagram, and LinkedIn and so on. The current systems assembled together the people on the basis of hobbies and people they already known. The Event or Group Based Friend Recommending System for Social Network is based on user’s activities.

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Fig. 1: Social Network

Recommendation Systems Recommendation systems represents a big role in providing quality customized or modified user experiences. A recommender system is a personalization technique that helps users to discover items of interest based on their likings. Recommender system is an effective tools that overcomes the information overloaded problem by providing users with the most significant contents. Recommendation systems on the web were first popularized by Amazon.com, which would show users personalized recommendations of items that the system thought they would like based on the items that they had bought or rated in the past. Since then, the practice has spread widely, as computing power becomes cheaper and as the algorithms become more widespread. Recommender systems are the ones used to recommend friends to the users based on some criteria. They are normally used to handle and solve the information overload. Recommender systems are gaining widespread acceptance in ecommerce applications as a way of tackling the information overload problem. This problem affects our everyday experience while searching for information on a topic. To overcome this problem, often rely on suggestions from others who have more experience on the topic. However, in the Web case where there are numerous suggestions, it is not easy to detect the trustworthy ones. The process of recommendation becomes controllable by shifting from individual to collective suggestions. Three parallel approaches have emerged in the context of recommender systems: collaborative filtering (CF), content-based filtering (CB) and hybrid methods. Recommendation systems on the web service were first promoted by Amazon, which displays the users personalized recommendation of the items that the system thought they would like based on items that they had been visited, purchased or rated in the past. Recommendation systems that try to suggest items (e.g., music, movie, and books) to users have become more and more popular in recent years. For instance, Amazon recommends items to a user based on items the user previously visited, and items that other users are looking at. Netflix and Rotten Tomatoes recommend movies to a user based on the user’s previous ratings and watching habits. Recently, with the advance of social networking systems, friend recommendation has received a lot of attention. Generally speaking, existing friend recommendation in social networking systems, e.g., Facebook, LinkedIn and Twitter, recommend friends to users if, according to their social relations, they share common friends. II. RELATED WORKS With the rapid development of Internet technology, the explosive growth of information makes it more and more difficult for users to find the information they need. Personalized recommendation system is the most common and effective tool solve the problem of the overload information. By establishing a user preference model, the system can find the user's information from the massive data and then recommend to the corresponding users to solve the problem of the information overload. The current personalized recommendation technology mainly divided into: content based recommendation, collaborative filtering recommendation, model based recommendation, knowledge based recommendation, context based recommendation, hybrid recommendation and so on. MatchMaker Matchmaker uses an collaborative filtering recommending system based on personality matching. The aim is to influence the social information and mutual understanding among the people in present social network connection and produce friend recommendation based on rich and high contextual data from physical world interactions of citizens. Matchmaker allow the users network to match them with the related TV characters, and then users relationships in the TV programs as a parallel association matrix to advice to users friends. The system recommends the user to become friends with someone whose corresponding TV character is friends with the users corresponding TV character. If the user has not watched the television show, he is likely to be curious in finding out what type of behavior or characteristics the recommended friend has through TV show. The benefit of the system is uses of the social information and mutual understanding among people. Personality matching provide more contextual

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knowledge about the recommended friends. For example let the users be A and B. The TV characters be X and Y. To recommend B as friend of A the following steps are followed. A matches with personality of X as per as friends rating. B matches with personality of Y as per Bs friends rating. If X and Y are friends, then system can recommend B as friend to A. The benefit of the system is uses of the social information and mutual understanding among people. Personality matching provide more contextual knowledge about the recommended friends. The main drawback is that the function is limited to TV show. Location Based Social Network Location based social network based on context and content filtering. Here user and location are the two major area closely associated or attached with each other in a location based social network. The method is adding a location to a present social network so that people in the social structure can distribute the location embedded information. In this paper they create two comparing methods to provide quality friend recommendation. First method combines current landmark and users dwell time at certain landmark and evaluate location similarity between users. The second method is to examine the interest list from network accounts by using pattern matching and finding longest common subsequence. Next the server searches for parallel users in a location based on location similarity and interest similarity. Based on this similarity an acceptable degree is decided. In this method they flopped to track activity of user in a location. Physical and Social Context Kwon and Kim proposed friend recommendation which is used in context aware applications. The context-aware systems provide the user with adaptive recommendations from enormous information. A challenging research issue in social computing is the recommendation method using context. The author proposes a friend recommendation method using physical and social context. The main idea of the proposed method is consisted of the following three stages, Firstly, it computes the friendship score based on similar behavior using physical context. The traditional information retrieval method, BM25 weighting scheme is used for computation. Secondly, the method computes friendship score with friend relation in the friendship graph using social context. Finally, all of the friendship scores are combined and then recommend friends by the scoring values. The spiritual friendship is computed by physical contexts and social friendship is computed by social contexts. The social friendship score is computed using distance between friends in the friendship graph. The physical contexts define the spiritual friendship and social friendship is computed by social contexts. The length of edges between nodes of graph that is distance between friends in the friendship graph is used for computing social friendship score. The main merit of this method is finding friends to satisfy user’s present context. However physical and social context is not clearly defined and how the information is extracted. The personalized recommendation system with friends-of-friends method to recommend new friends to users is provided by different social network sites. The drawback is that it is more probable a person will know a friend of their friends rather than a random person. However, this approach does not consider social interactions of the user. Proximity and Homophily Alvin Chin, Bin Xu, Hao Wang proposed a friend recommendation based on physical context. Here physical context is based on meetings and encounters. The method uses the intuition that people who meet for a conference can be recommended as friends. This will help the conference attendees to better organize their schedule and expand their social network. It builds a friend recommendation system using proximity and homophily. Proximity refers to physical context based on meetings and encounters. Homophily refers to shared contents, co-authored papers, commenting on same blog, common friends etc. The interactions between the users were captured by an application Find & Connect. This application uses location and encounters, together with the conference basic services to capture the user interactions. Here weight is assigned for each attribute of proximity and homophily. Then relevance vector is obtained for each user. Recommendation score is computed for each user. Then recommend top K users with the highest score. Recommendation mechanism based on physical context is better than FOF approach. This method provides a reason why you should a person as your friend i.e. you know each other before and have encountered before. The main drawback is that only indoor activity is considered. Trust Enhanced Recommendation of Friends D. Nagamalai, E. Renault, M. Dhanushkodi proposed a trust based friend recommendation system. Basic and behavioral attributes are extracted from the user profile. Users with similar interests are computed. The user preference learnt through real valued genetic algorithm based on individual features in efficient manner to increase the recommendation effectiveness. Then generate enhanced neighborhood set based on the trust propogation.The recommendations are made using collaborative filtering algorithm. Similarities of attributes between users are found by assigning weights to each attributes. The weights are assigned based on user preference. Genetic algorithms are used to generate the weight. Fitness value is used to check if optimization goal of good recommendation is obtained. It handles the sparsity problem using trust. The challenge of designing a collaborative filtering system is how to provide accurate recommendation with sparse user profile. Suppose the user profile is new, and the system was not able to capture the users preference due to the lack of ratings, system understands preference of the user by how frequent he uses the system. To enhance neighborhood set in order to provide accurate recommendation with sparse data use trust values. The main advantage of the system is that the weights are calculated by real value which enhances performance. It deals with

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sparsity problem in collaborative based friend recommendation .The trust value shows what degree a user A trusts another user B, if unknown to each other. It is calculated by difference of rating assigned by A and B to their common friends. Table – 1 Recommendation

III. PROPOSED SYSTEMS Most of the friend recommendation system depends on preexisting user relationships to gather the friend applicants. For example, Facebook relies on the social link analysis among those who already share mutual friends and suggest symmetrical users as a potential friend. The guidelines to group peoples together include habits or lifestyle, attitudes etc. Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. In this paper, we present a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. Human activity based friend recommendation system, discovers life styles of users from user-centric sensor data and, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. We model a user’s daily life as life documents, from which his/her life styles are extracted. Here user’s daily life is demonstrated as life documents from which the user’s lifestyles are extracted. Then measure the similarity of lifestyle between users and calculate users. It also take part a linear feedback procedure to improve the recommendation accuracy. A social network is defined as a social structure of people having relationship based on casual interests e.g. friendship and honesty. Social network system focuses on the structure and identification of on-line social networks for those who share their interests and activities or those who are interested in browsing others’ interests and activities. These networks, first, are used in order to making friends and sharing ideas among members but today they are used in order to do business and data sharing. Social networking sites also include sharing of files or data among the users or group of users. Data sharing is not easier and an accurate analysis on the shared data provides more benefits to both the society and individuals. Users can upload and download files in this recommendation system. While uploading the data, users want to enter file id, filename, key and whether to group or person and taking these attributes as input. Using Searchable Public-Key Ciphertexts with Hidden Structures data’s are encrypted to database and decryption also done. Using the file key user can download the data. Life Style Modeling In Life style modeling having so many activities and life style. In these Life styles and activities are reflection of daily live, where daily live can be a mixture of life styles and life style is a mixture of activities. This is analogous to the treatment of documents as ensemble of topics and topics as ensemble of words. By taking advantage of recent developments in the field of text mining, we model the daily lives of users as life documents, the life styles as topics, and the activities as words.

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Activity Recognition We need to first classify or recognize the activities of users. Life styles are usually reflected as a mixture of motion activities with different occurrence probability. Generally speaking, there are two mainstream approaches: supervised learning and unsupervised learning. In practice, the number of activities involved in the analysis is unpredictable and it is difficult to collect a large set of ground truth data for each activity, which makes supervised learning algorithms unsuitable for our system. Therefore, unsupervised learning approaches uses to recognize activities. Here, the popular K-means clustering algorithm to group data into clusters, where each cluster represents an activity. Note that activity recognition is not the main concern of our paper. Other more complicated clustering algorithms can certainly be used. Feedback Mechanism For optimization of the presentation, feedback control procedure is introduced into the system. Feedback is alsogiven by the user. Feedback can be in the form of query or the statement. Feedback is produced by the system and is analyzed on the basis of other users feedback. In other way one of the feedback is compared with the other feedback activity. The user can respond as feedback to the system. System stores such kind of feedback and when in future, this type of activity was occurs then the system directly considers the activity and increases the satisfaction of users. Upload/Download Files Data sharing with a large number of participants must take into account many issues, that is efficiency, data integrity and privacy of data owner. Data sharing is not easier, and an accurate analysis on the shared data provides more benefits to both the society and individuals. Users can upload and download files in this recommendation system. While uploading data, users want to enter file id, filename, file key and decide whether to group or person and taking these attributes as input. Using Searchable PublicKey Ciphertexts with Hidden Structures data’s are encrypted to the database. Using the file key user can download the data. Through message users can get the file key while downloading the data. In this concept, keyword searchable ciphertexts with their hidden structures can be generated in public key setting with a keyword search trapdoor, partial relations can be disclosed to guide the discovery of all matching ciphertexts. Semantic security is deﬁned for both the keywords and the hidden structures. It is noting that semantic security is suitable for keyword-searchable ciphertexts with any kind of hidden structures. IV. CONCLUSION Unlike the current friend recommendation schemes, which depend on the preexisting social relationships and geographical information, “Human Activity Based Friend Recommendation System” is a scheme where the friend suggestions are provided based on user’s daily activities. It captures the user’s daily activities and suggests friends to users if they share identical lifestyles. The similarity between each users in the database is been calculated using the similarity metrics, and friends are recommended in the cases of both users personal and similar interest and also based on his noninterest categories. We also take the feedback and query from the user regarding certain issue so that we can resolve the problem. We also obtained the feedback from the user about our recommended system. Data sharing provides more benefit to both the society and individuals. Using Searchable Public-Key Ciphertexts with Hidden Structures the datas are encrypted to the database. Using the file key user can download the data. Through message users can get the file key while downloading the data. In this concept, keyword searchable ciphertexts with their hidden structures can be generated in public key setting. Semantic security is deﬁned for both the keywords and the hidden structures It is noting that semantic security are suitable for keyword-searchable ciphertexts with any kind of hidden structures. REFERENCES [1] [2] [3] [4] [5] [6] [7]

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