Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

Measuring vertex centrality in co-occurrence graphs for online social tag recommendation

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of ECML PKDD (The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) Discovery Challenge 2009, Bled, Slovenia, September 7, 2009.We present a social tag recommendation model for collaborative bookmarking systems. This model receives as input a bookmark of a web page or scientific publication, and automatically suggests a set of social tags useful for annotating the bookmarked document. Analysing and processing the bookmark textual contents - document title, URL, abstract and descriptions - we extract a set of keywords, forming a query that is launched against an index, and retrieves a number of similar tagged bookmarks. Afterwards, we take the social tags of these bookmarks, and build their global co-occurrence sub-graph. The tags (vertices) of this reduced graph that have the highest vertex centrality constitute our recommendations, whThis research was supported by the European Commission under contracts FP6-027122-SALERO, FP6-033715-MIAUCE and FP6-045032 SEMEDIA. The expressed content is the view of the authors but not necessarily the view of SALERO, MIAUCE and SEMEDIA projects as a whol

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Promoting Social Media Dissemination of Digital Images Through CBR-Based Tag Recommendation

Multimedia content has become an essential tool to share knowledge, sell products or disseminate messages. Some social networks use multimedia content to promote information and create social communities. In order to increase the impact of the digital content, those images or videos are labeled with different words, denominated tags. In this paper, we propose a recommender system which analyzes multimedia content and suggests tags to maximize its influence in the social community. It implements a Case-Based Reasoning architecture (CBR), which allows to learn from previous tagged content. The system has been evaluated through cross fold validation with a training and validation sets carefully constructed and extracted from Instagram. The results demonstrate that the system can suggest good options to label our image and maximize the influence of the multimedia content

Collaborative Tagging and Taxonomy by Vector Space Approach

Collaborative tagging or group tagging is tagging performed by a group of users usually to support in re-finding the items. The limberness of tagging allows users to classify their collections of items in the ways that they find useful, but the personalized variety of expressions can present challenges when searching and browsing. When users can liberally choose tags (users create and apply public tags to online items as different to selecting terms from a proscribed terminology based on the users feedback), the resulting metadata can consist of homonyms (the same tags used with dissimilar implication) and synonyms (multiple tags for the same concept) which may direct to inappropriate connections between items and wasteful searches for information about a subject. Collaborative tagging requires the enforcement of method that enables users to protect their privacy by allowing them to hide certain user-generated contents without making them useless for the purposes they have been provided in a given online service. This means that privacy-preserving mechanisms must not harmfully affect the service truthfulness and usefulness.The proposed approach defends the user privacy to a certain level by reducing the tags that make a user profile let somebody see partiality toward certain categories of interest or feedback

On relational learning and discovery in social networks: a survey

The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements

Automated Social Text Annotation With Joint Multilabel Attention Networks

Automated social text annotation is the task of suggesting a set of tags for shared documents on social media platforms. The automated annotation process can reduce users' cognitive overhead in tagging and improve tag management for better search, browsing, and recommendation of documents. It can be formulated as a multilabel classification problem. We propose a novel deep learning-based method for this problem and design an attention-based neural network with semantic-based regularization, which can mimic users' reading and annotation behavior to formulate better document representation, leveraging the semantic relations among labels. The network separately models the title and the content of each document and injects an explicit, title-guided attention mechanism into each sentence. To exploit the correlation among labels, we propose two semantic-based loss regularizers, i.e., similarity and subsumption, which enforce the output of the network to conform to label semantics. The model with the semantic-based loss regularizers is referred to as the joint multilabel attention network (JMAN). We conducted a comprehensive evaluation study and compared JMAN to the state-of-the-art baseline models, using four large, real-world social media data sets. In terms of F 1 , JMAN significantly outperformed bidirectional gated recurrent unit (Bi-GRU) relatively by around 12.8%-78.6% and the hierarchical attention network (HAN) by around 3.9%-23.8%. The JMAN model demonstrates advantages in convergence and training speed. Further improvement of performance was observed against latent Dirichlet allocation (LDA) and support vector machine (SVM). When applying the semantic-based loss regularizers, the performance of HAN and Bi-GRU in terms of F 1 was also boosted. It is also found that dynamic update of the label semantic matrices (JMAN d ) has the potential to further improve the performance of JMAN but at the cost of substantial memory and warrants further study