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Link prediction is one of the most essential and crucial tasks in complex network research since it seeks to forecast missing links in a network based on current ones. This problem has applications in a variety of scientific disciplines, including social network research, recommendation systems, and biological networks. In previous work, link prediction has been solved through different methods such as path, social theory, topology, and similarity-based. The main issue is that path-based methods ignore topological features, while structure-based methods also fail to combine the path and structured-based features. As a result, a new technique based on the shortest path and topological features' has been developed. The method uses both local and global similarity indices to measure the similarity. Extensive experiments on real-world datasets from a variety of domains are utilized to empirically test and compare the proposed framework to many state-of-the-art prediction techniques. Over 100 iterations, the collected data showed that the proposed method improved on the other methods in terms of accuracy. SI and AA, among the existing state-of-the-art algorithms, fared best with an AUC value of 82%, while the proposed method has an AUC value of 84%. [ABSTRACT FROM AUTHOR]

The increased usage of the internet and social networks generates a large volume of information. Exploring through the large collection is time-consuming and hard to find the required one, so there is a serious need for a recommendation system. Based on this context several movie recommendation (MR) systems have been recently established. In addition, they have poor data analytics capability and cannot handle changing user preferences. As a result, there are many movies listed on the recommendation page, which provides for a poor user experience is the major issue. Therefore, in this work, a novel Taymon Optimized Deep Learning network (TODL net) for recommending top best movies based on their past choices, behaviour and movie contents. The deep neural network is a combination of Dilated CNN with Bi-directional LSTM. The DiCNN-BiLSTM model eliminates the functionality pooling operations and uses a dilated convolution layer to address the issue of information loss. The DiCNN is employed to learn the movie contents by mining user behavioral pattern attributes. The BiLSTM is applied to recommend the best movies on basis of the extracted features of the movie rating sequences of users in other social mediums. Moreover, for providing better results the DiCNN-BiLSTM is optimized with Taymon optimization algorithm to recommend best movies for the users. The proposed TODL net obtains the overall accuracy of 97.24% for best movies recommendation by using TMDB and MovieLens datasets. [ABSTRACT FROM AUTHOR]

Point-of-interest (POI) recommendation has become one of the research highlights in the field of recommender systems due to the prosperity of location-based social networks in recent years. Various techniques have been proposed to improve the performance of the personalized recommendation service. Embedding-based methods have shown promising effect and attracted great attention for their flexibility and efficiency. Bayesian Personalized Ranking (BPR), as a famous optimization algorithm, has been widely used to learning the parameters of Embedding-based models in the recommendation scenario. However, existing Bayesian Personalized Ranking and its follow-up methods ignore the unique user preference when constructing the positive and negative samples, leading a suboptimal performance. To overcome this limitation, we propose a novel method named preference-aware Bayesian Personalized Ranking (PABPR) according to empirical analyses on real-world datasets. The empirical analyses show that a user tend to visit a POI with categories which have been visited before. Thus, the key idea of PABPR is to introduce such user behaviors into the sample constructing process. PABPR is a general method which could be used for training various Embedding methods. Extensive experiments show that PABPR can lead a superior model performance compare to BPR and its variant methods. [ABSTRACT FROM AUTHOR]

With the rapid development of social networks, studying and analyzing their structures and behaviors has become one of the most important requirements of businesses. Social network analysis can be used for many different purposes such as product ads, market orientation detection, influential members detection, predicting user behaviors, recommender systems improvements, etc. One of the newest research topics in social network analysis is the enhancement of the information propagation performance in different aspects based on application. In this paper, a new method is proposed to improve few metrics such as distribution time and precision on social networks. In this method, the local attributes of nodes and also the structural information of the network is used to forward data across the network and reduce the propagation time. First of all, the centrality and Assortativity are calculated for all nodes separately to select two sets of nodes with the highest values for both criteria. Then, the initial active nodes of the network are selected by calculating the intersection of the two sets. Next, the distribution paths are detected based on the initial active nodes to calculate the propagation time. The performance analysis results show that the proposed method has better outcomes in comparison to other state-of-the-art methods in terms of distribution time, precision, recall, and AUPR criteria. [ABSTRACT FROM AUTHOR]

The recent privacy incidents reported in major media about global social networks raised real public concerns about centralized architectures. P2P social networks constitute an interesting paradigm to give back users control over their data and relations. While basic social network functionalities such as commenting, following, sharing, and publishing content are widely available, more advanced features related to information retrieval and recommendation are still challenging. This is due to the absence of a central server that has a complete view of the network. In this paper, we propose a new recommender system called P2PCF. We use collaborative filtering approach to recommend content in P2P social networks. P2PCF enables privacy preserving and tackles the cold start problem for both users and content. Our proposed approach assumes that the rating matrix is distributed within peers, in such a way that each peer only sees interactions made by her friends on her timeline. Recommendations are then computed locally within each peer before they are sent back to the requester. Our evaluations prove the effectiveness of our proposal compared to a centralized scheme in terms of recall and coverage. [ABSTRACT FROM AUTHOR]

Recommendation systems provide reliable and relevant recommendations to users and also enable users' trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user's friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users' opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility. [ABSTRACT FROM AUTHOR]

For the acquisition of user behavior preference in social network, usually a data mining will be conducted on the nearest neighborhood users or latestprojects based on the user's historical behavior data, so as to find similar behaviorrelationship for quantitative analysis; it can also focus on the awareness on the user-related context information based on cognitive psychology, so as to find its internal links forthe potential mining. However, these methods ignore the intrinsic link between the browsing behavior and the preferred topics in the user link connection, resulting in the limited precision and accuracy of the preference acquisition. Inspired by the theory of complex network link prediction and the topic model, anacquisition method for users' browsing behavior preference was proposed in this paper. In the multi-dimensional network link environment, by measuring the importance of the node via network centrality and search the social network link via setting the similarity threshold, the real-time multi-link information and the big data about users' browsing under each link were acquired, then the data were filteredand cleaned by using adjustable parameters. On this basis, according to the least squares criterion the data underwent a fusion and were used to construct a data node distribution model for user browsing behavior, then the frequent feature items of user browsing behavior preference were extracted. Based on the extracted feature terms, the variational Bayes approximation reasoning method was used to construct the preference topic model. Finally, the hierarchical VSM model representation method was used to establish the preference acquisition model of user browsing behavior, and the model was updated in real time by user feedback processing mechanism. The experimental results on the real data set showed that the link search method and the preference topic model provided by this paper are accurate and efficient. Compared with the classical cooperative filtering method and the context-awareness method, the precision, accuracy and effectiveness of the preference acquisition model provided this paper are significantly improved, and its adaptability has been significantly strengthened. [ABSTRACT FROM AUTHOR]

Personal information management enables users to manage and classify information via the social tagging. The personal information management platform has recently successfully adopted social networks, enabling users to conveniently share their preferences of information with each other. The emerging social networks generate new concepts for designing modern recommender systems in personal information management and sharing platforms. To design a recommender mechanism for the personal information management and sharing platforms, this work incorporates tag-based personalized interest and social network relationships into a modified Bayesian probability model. The proposed system is demonstrated with experimental datasets obtained from a popular social resource sharing website. The performances of the proposed system are evaluated based on the word2vec word embedding model. Experimental results indicate that incorporating social network information and personalized tag-based preference with the Bayesian model can improve the recommendation quality for social information sharing websites. [ABSTRACT FROM AUTHOR]

Cyber-physical Social Network (CPSN) becomes an essential component of daily life. In recent years, CPSN has dragged millions of users to convey their social opinions. It is highly desirable to mine influential features from such diverse data to make a prediction on users' Point of Interest (POI). Notably, social ties of the user in a specific location have a tendency to share similar opinions. Thus with the appearance of social links, the location based recommendations become popular to acquire reliable POI recommendations. Collaborative Filtering Recommender System (CFRS) able to discover reliable POI recommendations for the target user based on Location-based CPSN. To enhance the performance of CFRS, a clustering ensemble model is proposed in this article. Four different swarm intelligent based cluster optimization algorithms were utilized to generate finite clusters. The experiment is conducted on two real-time social network dataset to exhibit the performance of the proposed CE-CFRS. The result shows that the clustering ensemble model outperforms a single clustering model in terms of assessment metrics. [ABSTRACT FROM AUTHOR]

Online judges are online systems that test solutions in programming contests and practice sessions. They tend to become large live repositories of problems, with hundreds, or even thousands, of problems. This wide problem statement availability becomes a challenge for new users who want to choose the next problem to solve depending on their knowledge. This is due to the fact that online judges usually lack meta information about the problems and the users do not express their own preferences either. Nevertheless, online judges collect a rich information about which problems have been attempted, and solved, by which users. In this paper, we consider all this information as a social network, and use social network analysis techniques for creating similarity metrics between problems that can be then used for recommendation. [ABSTRACT FROM AUTHOR]

Discovering an effective team of experts toward accomplishing the specific task in social networks has been considered in many real projects. The communication and collaboration among the members and the small cardinality of the team are in the opposite direction to success of the projects. In this paper, we show that the type of a similarity function is also impressive. Its importance is revealed on determining what similar or dissimilar experts should be selected or rejected in the process of the assignment. Considering the graph of underlying social network as conceptual social networking websites, we attribute the team formation problem as a vertices similarity environment based on their common neighbors regarding their co-authored papers. Also, the implicit similarities are used with respect to inattention of additional intermediates between any two nodes in the graph. In addition, taking inspiration from human-human interactions, using just the implicit vertex similarities propose a collaborative recommendation that is based on the team formation framework. They can also identify effectors in social networks established by the structural equivalence relation. Thus, they make the algorithm faster on searching for members of the team. Moreover, the proficiency similarity measures of authors are considered as their potential characteristics that measure their skillfulness level and real contribution corresponding to the required skill. The combination of similarity measures in the cost function causes the algorithm to search the more effective team specially in equal situations. The experimental results on DBLP co-authorship graph show the effectiveness of using the new similarity measures in the proposed method. [ABSTRACT FROM AUTHOR]

Recommendation techniques that analyze social trust networks attracted much attention in the last few years. They recommend such items that are appreciated by trusted friends. In this paper, we explore how to use trust information for generating personalized document recommendations such as for scientific papers or for webpages. The basic idea is to jointly analyze a trust network between readers who review the documents and the reference network between the documents. We develop trust-enhanced visibility measures for measuring the quality and the importance of documents and evaluate them in simulation studies. [ABSTRACT FROM AUTHOR]

Collaborative filtering recommender systems are typically unable to generate adequate recommendations for newcomers. Empirical evidence suggests that the incorporation of a trust network among the users of a recommender system can significantly help to alleviate this problem. Hence, users are highly encouraged to connect to other users to expand the trust network, but choosing whom to connect to is often a difficult task. Given the impact this choice has on the delivered recommendations, it is critical to guide newcomers through this early stage connection process. In this paper, we identify several classes of key figures in the trust network, namely mavens, frequent raters and connectors. Furthermore, we introduce measures to assess the influence of these users on the amount and the quality of the recommendations delivered by a trust-enhanced collaborative filtering recommender system. Experiments on a dataset from Epinions.com support the claim that generated recommendations for new users are more beneficial if they connect to an identified key figure compared to a random user. [ABSTRACT FROM AUTHOR]