Social recommender system, using social relation networks as additional input to improve the accuracy of traditional recommender systems, has become an important research topic. However, most existing methods utilize the entire user relationship network with no consideration to its huge size, sparsity, imbalance, and noise issues. This may degrade the efficiency and accuracy of social recommender systems. This study proposes a new approach to manage the complexity of adding social relation networks to recommender systems. Our method first generates an individual relationship network (IRN) for each user and item by developing a novel fitting algorithm of relationship networks to control the relationship propagation and contracting. We then fuse matrix factorization with social regularization and the neighborhood model using IRN's to generate recommendations. Our approach is quite general, and can also be applied to the item-item relationship network by switching the roles of users and items. Experiments on four datasets with different sizes, sparsity levels, and relationship types show that our approach can improve predictive accuracy and gain a better scalability compared with state-of-the-art social recommendation methods.

An Approach for Building Efficient and Accurate Social Recommender Systems Using Individual Relationship Networks

Pepper bell leaf disease detection and classification using optimized convolutional neural network

The growth in the usage of the web, especially e-commerce website, has led to the development of recommender system (RS) which aims in personalizing the web content for each user and reducing the cognitive load of information on the user. However, as the world enters Big Data era and lives through the contemporary data explosion, the main goal of a RS becomes to provide millions of high quality recommendations in few seconds for the increasing number of users and items. One of the successful techniques of RSs is collaborative filtering (CF) which makes recommendations for users based on what other like-mind users had preferred. Despite its success, CF is facing some challenges posed by Big Data, such as: scalability, sparsity and cold start. As a consequence, new approaches of CF that overcome the existing problems have been studied such as Singular value decomposition (SVD). This paper surveys the literature of RSs and reviews the current state of RSs with the main concerns surrounding them due to Big Data. Furthermore, it investigates thoroughly SVD, one of the promising approaches expected to perform well in tackling Big Data challenges, and provides an implementation to it using some of the successful Big Data tools (i.e. Apache Hadoop and Spark). This implementation is intended to validate the applicability of, existing contributions to the field of, SVD-based RSs as well as validated the effectiveness of Hadoop and spark in developing large-scale systems. The implementation has been evaluated empirically by measuring mean absolute error which gave comparable results with other experiments conducted, previously by other researchers, on a relatively smaller data set and non-distributed environment. This proved the scalability of SVD-based RS and its applicability to Big Data.

/pdf/recommender-systems-in-light-of-big-data-bzswj4axbf.pdf

Recommender Systems in Light of Big Data

Recommendation systems can help internet users to find interesting things that match more with their profile. With the development of the digital age, recommendation systems have become indispensable in our lives. On the one hand, most of recommendation systems of the actual generation are based on Collaborative Filtering (CF) and their effectiveness is proved in several real applications. The main objective of this paper is to improve the recommendations provided by collaborative filtering using clustering. Nevertheless, taking into account the intrinsic relationship between users can enhance the recommendations performances. On the other hand, cooperative game theory techniques such as Shapley Value, take into consideration the intrinsic relationship among users when creating communities. With that in mind, we have used SV for the creation of user communities. Indeed, our proposed algorithm preforms into two steps, the first one consists to generate communities user based on Shapley Value, all taking into account the intrinsic properties between users. It applies in the second step a classical collaborative filtering process on each community to provide the Top-N recommendation. Experimental results show that the proposed approach significantly enhances the recommendation compared to the classical collaborative filtering and k-means based collaborative filtering. The cooperative game theory contributes to the improvement of the clustering based CF process because the quality of the users communities obtained is better.

A New Collaborative Filtering Approach Based on Game Theory for Recommendation Systems

Accurate Item Recommendation Algorithm of itemrank based on tag and context information

Recommender systems provide an important response to the information overload problem as it presents users more practical and personalized information services. Collaborative Filtering technique is the most successful in the recommender systems field. Collaborative filtering creates suggestions for users based on their neighbors preferences. But it suffers from poor accuracy, scalability and cold start problems. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender systems in which high quality recommendations are required and more recommendations per second for millions of customers and products need to be performed. Thus, the enhancement of scalability and efficiency of collaborative filtering (CF) algorithms become progressively more important and difficult. This paper focuses on study of different collaborative filtering algorithms taking into consideration the scalability issue. The different algorithms studied are cluster based, item based and context based.

/pdf/study-of-collaborative-filtering-recommendation-algorithm-1m8r4n3a9y.pdf

Study of Collaborative Filtering Recommendation Algorithm - Scalability Issue

The rapid growth in information on the World Wide Web has created different challenges to the users such as finding relevant and useful information and knowledge. To deal with such problem, recommender systems came into existence. Collaborative filtering techniques have gained much more popularity than other techniques in recommender system. As in real life, we ask our friends for different recommendations. But traditional systems ignore social relationships among users. In order to resolve this problem and improve recommender system's results, the idea of using social recommender system was discussed which contains the capability of identifying user's interests and preferences and their social network relationship. However, this approach is not sensitive to those users whose friends have dissimilar tastes. To tackle the problem of inaccuracy in result due to information deficiency, the social regularization term is used to impose constraints between one user and their friends individually.

Social recommender system by embedding social regularization

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 more similar tastes. This proposed approach resulted in more precise and realistic results than traditional system.

Enhanced Hybrid Recommender System using Social Friend Network

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Study of Collaborative Filtering Recommendation Algorithm - Scalability Issue

Social recommender system by embedding social regularization

Enhanced Hybrid Recommender System using Social Friend Network