Fabiola S. F. Pereira

On Using Temporal Networks to Analyze User Preferences Dynamics

User preferences are fairly dynamic, since users tend to exploit a wide range of information and modify their tastes accordingly over time. Existing models and formulations are too constrained to capture the complexity of this underlying phenomenon. In this paper, we investigate the interplay between user preferences and social networks over time. We propose to analyze user preferences dynamics with his/her social network modeled as a temporal network. First, we define a temporal preference model for reasoning with preferences. Then, we use evolving centralities from temporal networks to link with preferences dynamics. Our results indicate that modeling Twitter as a temporal network is more appropriated for analyzing user preferences dynamics than using just snapshots of static network.

Tree pattern mining with tree automata constraints

Most work on pattern mining focuses on simple data structures such as itemsets and sequences of itemsets. However, a lot of recent applications dealing with complex data like chemical compounds, protein structures, XML and Web log databases and social networks, require much more sophisticated data structures such as trees and graphs. In these contexts, interesting patterns involve not only frequent object values (labels) appearing in the graphs (or trees) but also frequent specific topologies found in these structures. Recently, several techniques for tree and graph mining have been proposed in the literature. In this paper, we focus on constraint-based tree pattern mining. We propose to use tree automata as a mechanism to specify user constraints over tree patterns. We present the algorithm CoBMiner which allows user constraints specified by a tree automata to be incorporated in the mining process. An extensive set of experiments executed over synthetic and real data (XML documents and Web usage logs) allows us to conclude that incorporating constraints during the mining process is far more effective than filtering the interesting patterns after the mining process.

Evolving Centralities in Temporal Graphs: A Twitter Network Analysis

In online social media systems users are not only posting, consuming, and sharing content, but also creating new and destroying existing connections in the underlying social network. This behavior lead us to investigate how user structural position reacts with the evolution of the underlying social network structure. While centrality metrics have been studied in the past, much less is known about their temporal behaviors and processing, mainly when analyzing not just networks snapshots, but interval graphs. Here, we study Twitter follower/followee network and how users centralities evolve over time. Our analysis is founded on temporal graphs theory. First, we model Twitter as a temporal network and revisit the concept of shortest path considering the time dimension. We show how to compute closeness and betweenness centralities using fastest paths. Then, we propose a baseline algorithm for mining streams of temporal networks. The task is to find all pairs fastest paths inside an observation window. We find that Twitter users are fairly dynamic and from one moment to the next, they can assume (or leave) central roles in the network.

Visual Perception Similarities to Improve the Quality of User Cold Start Recommendations

Recommender systems are well-know for taking advantage of available personal data to provide us information that best fit our interests. However, even after the explosion of social media on the web, hence personal information, we are still facing new users without any information. This problem is known as user cold start and is one of the most challenging problems in this field. We propose a novel approach, VP-Similarity, based on human visual attention for addressing this problem. Our algorithm computes visual perceptions similarities among users to build a visual perception network. Then, this networked information is provided to recommender system to generate recommendations. Experimental results validated that VP-Similarity achieves high-quality ranking results for user cold start recommendation.

Integrating user preference to similarity queries over medical images datasets

Large amounts of images from medical exams are being stored in databases, so developing retrieval techniques is an important research problem. Retrieval based on the image visual content is usually better than using textual descriptions, as they seldom gives every nuances that the user may be interested in. Content-based image retrieval employs the similarity among images for retrieval. However, similarity is evaluated using numeric methods, and they often orders the images by similarity in a way rather distinct from the users intention. In this paper, we propose a technique to allow expressing the users preference over attributes associated to the images, so similarity queries can be refined by preference rules. Experiments performed over a dataset with computed tomography lung images shows that correctly expressing the users preferences, the similarity query precision can increase from an average of 60% up to close to 100%, when enough interesting images exists in the database.

VP-Rec: A Hybrid Image Recommender Using Visual Perception Network

A requirement for a great user experience is to meet the exact needs for the usage of a recommender system. Such systems need users historical preferences to reasonably perform, which might not be the case for a cold-start user. This paper presents VP-Rec, a hybrid image recommender system that addresses the new user cold-start problem. VP-Rec combines user visual perception and pairwise preferences as source of information to perform recommendations. First, we infer pairwise preferences from users ratings. Next, we build visual perception networks linking users according to their visual attention similarities. From these two inferred structures, we build consensual prediction models, so that when a new user enters the system, we capture his visual attention and choose the best model that fits him. The system has been tested on two image datasets, getting important improvements in terms of ranking quality (nDCG) when applied to new user cold-start scenario against state-of-art recommender systems.

Processing Evolving Social Networks for Change Detection Based on Centrality Measures

Social networks have an evolving characteristic due to the continuous interaction between users, with nodes associating and disassociating with each other as time flies. The analysis of such networks is especially challenging, because it needs to be performed with an online approach, under the one-pass constraint of data streams. Such evolving behavior leads to changes in the network topology that can be investigated under different perspectives. In this work we focus on the analysis of nodes position evolution—a node-centric perspective. Our goal is to spot change-points in an evolving network at which a node deviates from its normal behavior. Therefore, we propose a change detection model for processing evolving network streams which employs three different aggregating mechanisms for tracking the evolution of centrality metrics of a node. Our model is space and time efficient with memory less mechanisms and in other mechanisms at most we require the network of current time step T only. Additionally, we also compare the influence on different centralities’ fluctuations by the dynamics of real-world preferences. Consecutively, we apply our model in the user preference change detection task, reaching competitive levels of accuracy on Twitter network.

Social network analysis: An overview

Social network analysis (SNA) is a core pursuit of analyzing social networks today. In addition to the usual statistical techniques of data analysis, these networks are investigated using SNA measures. It helps in understanding the dependencies between social entities in the data, characterizing their behaviors and their effect on the network as a whole and over time. Therefore, this article attempts to provide a succinct overview of SNA in diverse topological networks (static, temporal, and evolving networks) and perspective (ego‐networks). As one of the primary applicability of SNA is in networked data mining, we provide a brief overview of network mining models as well; by this, we present the readers with a concise guided tour from analysis to mining of networks.

On analyzing user preference dynamics with temporal social networks

The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks in information retrieval and recommendation systems domains. However, existing models are too constrained for capturing the complexity of the underlying phenomenon. Online social networks contain rich information about social interactions and relations. Thus, these become an essential source of knowledge for the understanding of user preferences evolution. In this work, we investigate the interplay between user preferences and social networks over time. First, we propose a temporal preference model able to detect preference change events of a given user. Following this, we use temporal networks concepts to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Finally, we look for a correlation between preference change events and node centrality change events over Twitter and Jam social music datasets. Our findings show that there is a strong correlation between both change events, specially when modeling social interactions by means of a temporal network.