JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 10, 2020 Social Network Recommender System Based on Collaborative Filtering Techniques Mr. Pitamber Adhikari1, Mrs. Renuka Sharma2 1Dept. of Computer Science and Engineering 2Dept. of Information Technology Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh [email protected], [email protected] Received: 6 February 2020 Revised and Accepted: 21 May 2020 ABSTRACT: Recommender Systems (RS) are used in different areas for applications like recommending products to customers. During this paper, this paper proposes a new approach for Recommender systems that employ the users’ social network to produce better recommendations for media items like YouTube, Amazon, Netflix, and Pandora. This paper has study personalized item recommendations within an enterprise social media application suite that features blogs, bookmarks, communities, Wiki, and shared files. Recommendations are supported by two of the core elements of social media people and tags. Relationship information among people, tags, and items, is collected and aggregated across different sources within the enterprise. This paper evaluated our recommended system through an intensive user study. Results show an excellent interest ratio for the tag- based recommended than for the people-based Recommender and even better performance for a combined Recommender. Tags applied to the user by the people are found to be highly effective in representing that user’s topics of interest. KEYWORDS: Recommender System, Social Network, Collaborative Filtering, Machine Learning. I. INTRODUCTION Social media has been enjoying an excellent deal of success in recent years, with a lot of users visiting social sites like LinkedIn, Instagram, Facebook, WhatsApp, for social networking, WordPress for blogging, Twitter for micro-blogging, Netflix, Flickr, Pandora radio, and YouTube for image and video sharing,[1] respectively, large amount of social news reading, and distinctive for social bookmarking. This social media networking relies principally on their users to make, and contribute content to annotate others' content with tagging, item ratings, and comments to create online relationships and to join online communities. To overcome information overload, Recommender systems became a key tool for providing users with personalized recommendations on items like movies, music, books, news, and sites. Intrigued by many practical applications, researchers have developed algorithms and systems over the last decade. A number of them are commercialized by online web services like Alibaba.com, Flipcart.com, Amazon.com, Netflix.com, and IMDb.com[2]. The recommender systems predict user preferences (often represented as numeric ratings) for new items that supported the user's past ratings on other items. There are typically three types of algorithms for recommender systems -- content-based methods and collaborative filtering and hybrid recommender. Content-based recommender systems measure the similarity of the recommended item to those that a target user likes or dislikes supported item attributes. On the opposite hand, the collaborative filtering method finds users with tastes that are almost like the target users supported their past ratings. Collaborative filtering will then make recommendations to the target user-supported the opinions of these similar users. Finding the most relevant information and items has been a challenge for an extended time[3]. Based on this user requirement, recommendation systems have become one of the foremost popular techniques to solve this problem. Almost, all recommendation systems try to make predictions about the preference of every user-supported the preferences of a group of comparable users through content-based filtering, collaborative filtering or combination of both. Figure 1 shows recommender system's types. 1277 JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 10, 2020 Figure 1: Types of Recommender System The collaborative filtering algorithm is the prevalent recommendation system which has been wont to identify users that may be characterized as the same consistent with the logged history of past user behaviors. Generally, a collaborative filtering algorithm uses a set of user profiles to find the most relevant information,[4] items, products, services or etc. for these users. The specific user achieves a recommendation that supported the user profiles of other similar users. There are several applications for collaborative filtering in several domains like trust and security, web services recommender systems. In the narrower sense, collaborative filters are a system by way of gathering tastes or taste knowledge from many users (collaboration), to make automatic assumptions (filtering) about a user's desires. As a co-operative filtering strategy, A is more likely to have the opinion of B on a matter if an individual A has the same opinion on a matter as a person B than a randomly-chosen participant. For example, a shared filtering recommendation program will anticipate a TV show a user wants to have a partial preference list for that user (likes or dislikes). More generally speaking, collaborator filtering is a process of filtering material or patterns through strategies involving cooperation between multiple operatives, points of view, data sources, etc. Several categories of data with the exception of: sensing and monitoring details, such as mineral exploration, environmental sensor across broad areas and several sensors; financial information, such as financial services entities which incorporate various financial sources; and online commerce and mobile application which focusses on user data, etc., have been obtained utilizing collective filtering procedures. Collaborative Filtering in Recommender System is shown in figure 2. 1278 JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 10, 2020 Figure 2: Collaborative Filtering in Recommender System II. SOCIAL NETWORK BASED RECOMMENDER SYSTEM A social network-based recommender system is defined as a social structure of individuals having a relationship supported by casual interests e.g. friendship and honesty. The social network focuses on the structure and person identification of online social networks for people who share their interests and activities or those that have an interest in browsing others’ interests and activities. These social networks, first, are employed to make friends and sharing ideas among members but today, they're employed to try to do business and data sharing. Of course, as time passes, two business and friendly environments are combined and alter into compound environments. As described above,[5] Media Scout is an application that has personalized media content via mobile devices and home TV. One of the features which exist within the system is the ability to send a request to another user within the system and to propose a friendship. The receiver of the invitation can accept or decline the invitation. If the second hand accepts the invitation, then the two users become ’friends’ of every other and may send recommendations for every other using one among the features within the application. This is often an identical scenario to the one among MSN messenger and of ICQ, apart from the usage goal. Within the two existing applications, one can chat with one’s friend and in Media Scout, a pair of friends can send movie recommendations for every other[6]. This implies that for every media item that the user watches she will be able to provide feedback on whether she liked it or not. This feedback helps the system to refine the profile of the user. For every user, the system keeps several items that she rated positively, several items that were rated negatively and a listing of friends moreover. 1279 JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 10, 2020 Figure 3 further illustrates how these three factors impact users' final buying decisions. Intuitively, a user's buying decision or rating is determined by both his/her own preference for similar items and his/her knowledge about the characteristics of the target item. A user's preference, like Angela’s interest in Hindi movies, is typically reflected from the user's past ratings to other similar items,[7] e.g. the number of Hindi movies that Angela previously viewed and therefore, the average rating that Angela gave to those movies. Knowledge about the target item will be obtained from public media like newspapers, media, and therefore, the web. Meanwhile, the feedback from friends is another source of data regarding the item, and that they are often more trustworthy than advertisements. When a user starts considering the feedback from his/her friends, he/she is then important by his/her friends. Note that this importance isn't limited thereto of our immediate friends. Distant friends also can cast their influence indirectly to us; e.g., Angela was influenced by Linda's office mate within the previous scenario[8]. Every one of those three factors has an effect on a user’s final buying decision. If the impact from all of them is positive, it's very likely that the target user will select the item. On the contrary, if any includes a negative item rating, e.g., very low ratings in other user reviews, the possibility that the target user will select the item will decrease. With such an understanding in mind, this paper is about to propose a social network-based recommender system within the following subsections. As