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Title
Abstract
Introduction
Background
Dataset
Review Correlations of Immediate Friends
Comparison Methods
Data Sparsity
Related Work
Conclusions
References
All Topics
Computer Science
Machine Learning

A Social Network-Based Recommender System (SNRS

Profile image of Jianming HeJianming He
https://doi.org/10.1007/978-1-4419-6287-4_4
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Abstract

Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this paper we present a new paradigm of recommender systems which can utilize information in social networks, including user preferences, an item's general acceptance, and influence from social friends. A probabilistic model is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large dataset reveals that friends have a tendency to select the same items and give similar ratings. Experimental results on this dataset show that our proposed system not only improves the prediction accuracy of recommender systems but also remedies the data sparsity and cold-start issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of our system by applying semantic filtering of social networks, and validate its improvement via a class project experiment. In this experiment we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most content providers.

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Recommender systems were created to guide the user in a personalized way to interesting resources and to help users cope with the problem of information overload. A system's ability to adapt to the users' needs is based on gathering user-generated collective intelligence. In this paper, we present WSNRS, the system proposed for recommending content within social networks. The main goal of the system is to identify and filter the recently published valuable resources while taking into account the interactions and the relationships the user has within social structures. The interactions are logged and aggregated in order to determine the trust scores between users. Using the scores obtained, one can identify the types of relationship established between users; the scores will then be integrated into an adaptive global model used for recommending resources. Our approach presents several advantages over classic CF-based approaches and content-based recommendations regarding cold start, scalability and serendipitous recommendations. We will illustrate this with a case study that we made using data provided by the implementation of the system in a real online social network.

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Recommender System from Personal Social Networks
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Recommender systems are found in many modern web sites for applications such as recommending products to customers. In this paper we propose a new method for recommender system that employs the users' social network in order to provide better recommendation for media items such as movies or TV shows. As part of this paper we develop a new paradigm for incorporating the feedback of the user's friends. A field study that was conducted on real subjects indicates the strengths and the weaknesses of the proposed method compared to other simple and classic methods. The system is envisioned to function as a service for recommending personalized media (audio, video, print) on mobile phones, online media portals, sling boxes, etc. It is currently under development within Deutsche Telekom Laboratories-Innovations of Integrated Communication projects.

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Recommender Systems & the Social Web
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Social trust relationships between users in social networks speak to the similarity in opinions between the users, both in general and in important nuanced ways. They have been used in the past to make recommendations on the web. New trust metrics allow us to easily cluster users based on trust. In this paper, we investigate the use of trust clusters as a new way of improving recommendations. Previous work on the use of clusters has shown the technique to be relatively unsuccessful, but those clusters were based on similarity rather than trust. Our results show that when trust clusters are integrated into memory-based collaborative filtering algorithms, they lead to statistically significant improvements in accuracy. In this paper we discuss our methods, experiments, results, and potential future applications of the technique.

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Collaborative Item Recommendations Based on Friendship Strength in Social Network
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International Journal of Machine Learning and Computing, 2020

Recommender Systems (RSs) are among the solutions in addressing the information overload problems. One of the RS main problem is cold start users where RSs do not have enough information to identify the user's preferences and thus unable to recommend relevant items. One of the approaches to overcome such problem is through social relationship which can be extracted from social networks. Existing studies used friendship relation to find nearest neighbors combined with implicit data such as tweet content, posts, like or tags to identify user's preference. This study proposed friendship strength through user's interaction and item rating values to identify users' preferences. User-based collaborative filtering methods are used in developing the system. Two datasets are synthetically obtained from MovieLens database for user's rating information. Meanwhile Twitter users' interactions data, was retrieved from Higgs Boson topic available from the Standford University. There are four phases of development namely user profile phase, friendship strength phase, user's preference phase and items recommendation phase. The findings of the study show that there is an improvement in the proposed method compared to the baseline based on the recall (R), precision (P) and F score measures.

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A Survey On Semantic Based Social Recommendation
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systems are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that user would give to an item. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. Collaborative Filtering is one of the most successful approaches to building recommender system, which uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are also recommender systems for experts, jokes, restaurants, financial services, life insurance, and Twitter followers. The Matrix Factorization methods have been proposed for social recommendation due to their efficiency in dealing with large data sets. This paper provides an overview of recommender systems that include Collaborative, Content and Matrix factorization methods.

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Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques.

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2ND Workshop on Recommender Systems and the Social Web
Bamshad Mobasher

Proceedings of the Fourth Acm Conference on Recommender Systems, 2010

In web-based social networks social trust relationships between users indicate the similarity of their needs and opinions. Trust in this sense can be used to make recommendations on the web because trust information enables the clustering of users based on their credibility which is aggregation of expertise and trustworthiness. In this paper, we propose a new approach to making recommendations based on leaders' credibility in the "Follow the Leader" model as Top-N recommenders by incorporating social network information into user-based collaborative filtering. To demonstrate the feasibility and effectiveness of "Follow the Leader" as a new approach to making recommendations, first we developed a Social Network Analysis Studio (SNAS) that captures real data from the Epinions dataset, next we used it to verify the proposed model. The empirical results incorporated in this paper, demonstrate that our approach is a significantly innovative approach to making effective CF based recommendations especially for cold start users.

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Enhanced Hybrid Recommender System using Social Friend Network
Shalmali Patil

International Journal of Management and Information Technology, 2014

Lots of people employ recommender systems to diminish the information overload over the internet. This leads the user in a personalized manner to hit upon interesting or helpful objects in a huge space of possible options. Amongst different techniques, Collaborative filtering recommender system has pulled off great success. But this technique pays no heed towards the social relationship of the users. This problem gave birth to the Social recommender system technology which possesses the capability to recognize user’s likings and preferences and their social relationships. In this paper, we present novel method where we combine collaborative filtering recommender system with social friend network to use social relationships. For this, we have made use of data related to users which provides their interests as well as their social relationship. Our method helps to find the friends with dissimilar tastes and determine the close friends amongst direct friends of targeted user which has ...

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Content-based Recommender System using Social Networks for Cold-start Users
Luiz Sergio de Souza

Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2017

Recommender systems have been widely applied to e-commerce to help customers find products to purchase. Cold-start is characterized by the incapability of recommending due to the lack of enough ratings. In fact, solutions for the cold-start problem have been proposed for different contexts, but the problem is still unsolved. This paper presents a RS for new e-commerce users by using only their interactions in social networks to understand their preferences. The proposed Recommender System (RS) applies a content-based approach and improves the experience of new users by recommending specific products in a preferred identified user category by analysing their data from the social network. Therefore, it combines three social network elements: (1) direct user posts (e.g.: "tweets" from Twitter and "posts" from Facebook), (2) content "likes" (e.g.: option "like" on a "post" or "tweet" posted by another user), and (3) page "likes" (e.g.: option "like" on a Facebook page). The proposed RS was tested for a retail e-commerce, which usually not only has a large range of categories of products, but also has products within these categories. The difficulty in predicting a product increases sharply with a greater number of categories and products. According to the experiment conducted, the proposed RS demonstrated to be a reasonable alternative to cold-start, i.e., for users accessing e-commerce for the very first time.

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An Overview of Recommender Systems on Social Networks Using LBSN
International Journal of Scientific Research in Science, Engineering and Technology IJSRSET

In case of new users these types of reviews plays a vital role in deciding whether to go for that specific service or not. We propose a system which works by rating behavior of social users to predict user service ratings users rating behaviors are focused. In our point of view the rating behavior in this system could be embedded with these aspects: 1) when user had rated the item, what is the rating of that item, 2) what is the item, 3) what are the rating interests of the user that we could find from his/her previous rating history. A factor, rating schedule to represent users daily rating behavior, people generally believe opinions of authorized people, people who are related to them and people who have enough knowledge in that specific domain, here the proposed system comes into play. In the proposed system we fuse four factors they are, user personal interest(related to item�s domain), interpersonal interest similarity between users(related to users interest), similarity in interpersonal rating behavior(related to users rating behavior), and diffusion in interpersonal rating behavior, into a unified matrix-factorized framework. A series of experiments are conducted in huge dataset. The new factors of social network like interpersonal exchange and interest based on circles of friends and challenges for recommender system (RS). Location data functions as the connection between user�s physical behaviors and social networks service by the smart phone or web services. We refer to these social networks know to geographical information as location-based social networks (LBSN).We mine:(1)user�s rating for any item.(2) between user�s rating differences and user-user.(3)interpersonal interest similarity, are a unified rating prediction modules are used to communicate with the user.

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