User-item Relationships Research Articles

Recommending human resources to project leaders using a collaborative filtering‐based recommender system: Case study of gitHub

Recommender systems (RSs) are a significant subclass of the information filtering system. RSs seek to predict the rating or preference that a user would give to an item in various online application community fields. Collaborative filtering (CF) is a technique which predicts user distinctions by learning past user-item relationships. However, it is hard to perceive the comparable interests between customers in light of the fact that the sparsity problem is caused by the deficient number of the relationship between users. It is a challenge which limited the ease of use of CF. This paper proposes a novel fuzzy C-means clustering approach which is used to deal with this sparsity problem by utilising a sparsest sub-graph detection algorithm in defining initial centres of the clustering method. The approach uses adaptability of fuzzy logic to make better personalised recommendations in terms of precision, recall and F-measure. The authors present a case study where GitHub is used to show the effectiveness of authors’ approach. Authors’ model can recommend relevant human resources (HR) to project leaders who have participated in similar projects. The comparative experiment results show that the planned approach will effectively solve the sparseness drawback and produce suitable coverage rate and recommendation quality.

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.

A Survey of Recommender Systems Based on Deep Learning

In recent years, deep learning’s revolutionary advances in speech recognition, image analysis, and natural language processing have gained significant attention. Deep learning technology has become a hotspot research field in the artificial intelligence and has been applied into recommender system. In contrast to traditional recommendation models, deep learning is able to effectively capture the non-linear and non-trivial user-item relationships and enables the codification of more complex abstractions as data representations in the higher layers. In this paper, we provide a comprehensive review of the related research contents of deep learning-based recommender systems. First, we introduce the basic terminologies and the background concepts of recommender systems and deep learning technology. Second, we describe the main current research on deep learning-based recommender systems. Third, we provide the possible research directions of deep learning-based recommender systems in the future. Finally, concludes this paper.

Recommendation in feature space sphere

Abstract Recently, recommendation algorithms have been widely used in many e-commerce platforms to recommend items to users on the basis of their preferences to improve selling efficiency. Matrix factorization methods which extract latent features of users and items by decomposing the rating matrix have achieved success in rating prediction. But almost all of these algorithms are designed to fit the rating matrix directly to get the latent features and ignore the user-item relationship in feature space. To this end, in this paper, we propose a recommendation in feature space sphere (RFSS) which takes into account the relationship between users and items in feature space. Different from the conventional latent feature based recommendation algorithms, the proposed algorithm supposes that if a user likes an item, the user is close to the item in feature space. Meanwhile, the closer a user and an item are in feature space, the higher the predicted rating will be. And an adaptive user-dependent coefficient is introduced to map the user-item distances to the predicted ratings. Extensive experiments on four real-world datasets have been conducted, the results of which show that our proposed method outperforms the state-of-the-art recommendation algorithms.

User Preference Learning for Online Social Recommendation

A social recommendation system has attracted a lot of attention recently in the research communities of information retrieval, machine learning, and data mining. Traditional social recommendation algorithms are often based on batch machine learning methods which suffer from several critical limitations, e.g., extremely expensive model retraining cost whenever new user ratings arrive, unable to capture the change of user preferences over time. Therefore, it is important to make social recommendation system suitable for real-world online applications where data often arrives sequentially and user preferences may change dynamically and rapidly. In this paper, we present a new framework of online social recommendation from the viewpoint of online graph regularized user preference learning (OGRPL), which incorporates both collaborative user-item relationship as well as item content features into an unified preference learning process. We further develop an efficient iterative procedure, OGRPL-FW which utilizes the Frank-Wolfe algorithm, to solve the proposed online optimization problem. We conduct extensive experiments on several large-scale datasets, in which the encouraging results demonstrate that the proposed algorithms obtain significantly lower errors (in terms of both RMSE and MAE) than the state-of-the-art online recommendation methods when receiving the same amount of training data in the online learning process.

A Recommendation System Based on Regression Model of Three-Tier Network Architecture

The sparsity problem of user-item matrix is a major obstacle to improve the accuracy of the traditional collaborative filtering systems, and, meanwhile, it is also responsible for cold-start problem in the collaborative filtering approaches. In this paper, a three-tier network Architecture, which includes user relationship network, item similarity network, and user-item relationship network, is constructed using comprehensive data among the user-item matrix and the social networks. Based on this framework, a Regression Model Recommendation Approach (RMRA) is established to calculate the correlation score between the test user and test item. The correlation score is used to predict the test user preference for the test item. The RMRA mines the potential information among both social networks and user-item matrix to improve the recommendation accuracy and ease the cold-start problem. We conduct experiment based on KDD 2012 real data set. The result indicates that our algorithm performs superiorly compared to traditional collaborative filtering algorithm.

Vulnerabilities and countermeasures in context-aware social rating services

Social trust and recommendation services are the most popular social rating systems today for service providers to learn about the social opinion or popularity of a product, item, or service, such as a book on Amazon, a seller on eBay, a story on Digg or a movie on Netflix. Such social rating systems are very convenient and offer alternative learning environments for decision makers, but they open the door for attackers to manipulate the social rating systems by selfishly promoting or maliciously demoting certain items. Although a fair amount of effort has been made to understand various risks and possible defense mechanisms to counter such attacks, most of the existing work to date has been devoted to studying specific types of attacks and their countermeasures. In this article, we argue that vulnerabilities in social rating systems and their countermeasures should be examined and analyzed in a systematic manner. We first give an overview of the common vulnerabilities and attacks observed in some popular social rating services. Next, we describe three types of attack strategies in two types of social rating systems, including a comprehensive theoretical analysis of their attack effectiveness and attack costs. Three context-aware countermeasures are then presented: (i) hiding user-item relationships, (ii) using confidence weight to distinguish popular and unpopular items, and (iii) incorporating time windows in trust establishment. We also provide an in-depth discussion on how these countermeasures can be used effectively to improve the robustness and trustworthiness of the social rating services.