Recently, learning embeddings of nodes in graphs has attracted increasing research attention. There are two main kinds of graph embedding methods, i.e., transductive embedding methods and inductive embedding methods. The former focuses on directly optimizing the embedding vectors, and the latter tries to learn a mapping function for the given nodes and features. However, little work has focused on applying the learned model from one graph to another, which is a pervasive idea in Computer Vision or Natural Language Processing. Although some of the graph neural networks (GNNs) present a similar motivation, none of them considers graph biases between graphs. In this paper, we present a novel graph embedding problem called Adaptive Task (AT), and propose a unified framework for the adaptive task, which introduces two types of alignment to learn adaptive node embeddings across graphs. Then, based on the proposed framework, a novel Graph Adaptive Embedding network (GraphAE) is designed to address the adaptive task. Furthermore, we extend GraphAE to a multi-graph version to consider a more complex adaptive situation. The extensive experimental results demonstrate that our model significantly outperforms the state-of-the-art methods, and also show that our framework can make a great improvement over a number of existing GNNs.

Many real-world networks have been evolving, and are finely modeled as temporal graphs from the viewpoint of the graph theory. A temporal graph is informative, and always contains two types of information, i.e., the temporal information and topological information, where the temporal information reflects the time when the relationships are established, and the topological information focuses on the structure of the graph. In this paper, we perform time-topology analysis on temporal graphs to extract useful information. Firstly, a new metric named T-cohesiveness is proposed to evaluate the cohesiveness of a temporal subgraph. It defines the cohesiveness of a temporal subgraph from the time and topology dimensions jointly. Specifically, given a temporal graph G s = ( Vs , ε Es ), cohesiveness in the time dimension reflects whether the connections in G s happen in a short period of time, while cohesiveness in the topology dimension indicates whether the vertices in V s are densely connected and have few connections with vertices out of G s . Then, T-cohesiveness is utilized to perform time-topology analysis on temporal graphs, and two time-topology analysis methods are proposed. In detail, T-cohesiveness evolution tracking traces the evolution of the T-cohesiveness of a subgraph, and combo searching finds out all the subgraphs that contain the query vertex and have T-cohesiveness larger than a given threshold. Moreover, a pruning strategy is proposed to improve the efficiency of combo searching. Experimental results confirm the efficiency of the proposed time-topology analysis methods and the pruning strategy.

Most of the previous work on recommender systems focuses on discovering novel items that meet users' personalized interest. But there is barely any study about recommending repeat items that consumed by the target user before. In fact, people's consumption behaviors are a mixture of repeat and novelty-seeking behaviors [1]. Since people forget about things as time elapses, it is possible that users may prefer the previously consumed items but cannot remember them at certain times. Therefore, Recommendation for Repeat Consumption (RRC) has some real utility and should be studied in depth. Some efforts have been done in related work [1]. However, they only consider item popularity and recency effect, and fail to take full advantage of behavioral features. In this paper, we attempt to address the RRC problem (illustrated in Fig 1) by proposing a Time-Sensitive Personalized Pairwise Ranking (abbr. TS-PPR) model based on the behavioral features extracted from user implicit feedback in the consumption history. TS-PPR factorizes the temporal useritem interactions via learning the mappings from the behavioral features in observable space to the preference features in latent space, and combines users static and dynamic preferences together in recommendation. An empirical study on real-world data sets shows encouraging results.

Among the previous studies in modeling users' preference on images, most of them assume there is a consistent ranking of images, and users' preference is transitive. That is, if a user likes image A over B and B over C, it must have A over C for this user. This condition holds when user compares images from a single angle. However, if there are multiple angles to consider, users' preference may not be transitive at all. Thus, it is interesting to know whether users' pairwise preference on images can be intransitive, and how can such personalized intransitivity be modeled.

Most of the previous studies on the user preferences assume that there is a personal transitive preference ranking of the consumable media like images. For example, the transitivity of preferences is one of the most important assumptions in the recommender system research. However, the intransitive relations have also been widely observed, such as the win/loss relations in online video games, in sport matches, and even in rock-paper-scissors games. It is also found that different subjects demonstrate the personalized intransitive preferences in the pairwise comparisons between the applicants for college admission. Since the intransitivity of preferences on images has barely been studied before and has a large impact on the research of personalized image search and recommendation, it is necessary to propose a novel method to predict the personalized intransitive preferences of images. In this paper, we propose the novel Multi-Criterion preference (MuCri) models to predict the intransitive relations in the image preferences. The MuCri models utilize different kinds of image content features as well as the latent features of users and images. Meanwhile, a new data set is constructed in this paper, in order to evaluate the performance of the MuCri models. The experimental evaluation shows that the MuCri models outperform all the baselines. Due to the interdisciplinary nature of this topic, we believe it would widely attract the attention of researchers in the image processing community as well as in other communities, such as machine learning, multimedia, and recommender system.

Recommender system has been studied as a useful tool to discover novel items for users while fitting their personalized interest. Thus, the previously consumed items are usually out of consideration due to the “lack” of novelty. However, as time elapses, people may forget those previously consumed and preferred items which could become “novel” again. Meanwhile, repeat consumption accounts for a major portion of people's observed activities; examples include: eating regularly at a same restaurant, or repeatedly listening to the same songs. Therefore, we believe that recommending repeat consumption will have a real utility at certain times. In this paper, we formulate the problem of recommendation for repeat consumption with user implicit feedback. A time-sensitive personalized pairwise ranking (TS-PPR) method based on user behavioral features is proposed to address this problem. The proposed method factorizes the temporal user-item interactions via learning the mappings from the behavioral features in observable space to the preference features in latent space, and combines users' static and dynamic preferences together in recommendation. An empirical study on real-world data sets shows encouraging results.

The directionality is a significant but inherent property of social ties, though usually ignored in undirected social networks due to its invisibility. However, we believe most social ties are natively directed, and the perception of directionality can improve our understanding about the network structures and further benefit other tasks upon social networks. In this study, we address the latent tie direction inference problem in undirected social networks. We engage in the investigation of directionality on real-world large-scale directed social networks and summarize our findings using four patterns. Upon that we propose a family of ReDirect approaches, including ReDirect-N, ReDirect-T and ReDirect-One, to inferring the hidden directions of undirected social ties based on the network topology only. ReDirect can incorporate with other predictive tasks, and introduce supervision to improve performance. We also present a simple but effective strategy to construct self-labeled data. Experimental results show that even without external information, our approach can recover the directions of networks effectively. Moreover, we find the ReDirect approaches can benefit the predictive tasks remarkably in an experimental study on link prediction. The ReDirect family can be a beneficial general data preprocess tool for various network analysis tasks by uncovering the hidden directions.

Large-scale distributed computing has made available the resources necessary to solve "AI-hard" problems. As a result, it becomes feasible to automate the processing of such problems, but accuracy is not very high due to the conceptual difficulty of these problems. In this paper, we integrated crowdsourcing with MapReduce to provide a scalable innovative human-machine solution to AI-hard problems, which is called CrowdMR. In CrowdMR, the majority of problem instances are automatically processed by machine while the troublesome instances are redirected to human via crowdsourcing. The results returned from crowdsourcing are validated in the form of CAPTCHA (Completely Automated Public Turing test to Tell Computers and Humans Apart) before adding to the output. An incremental scheduling method was brought forward to combine the results from machine and human in a "pay-as-you-go" way.

Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more “human-minded” when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model. Multiple implementations of the framework were investigated and compared. The experimental evaluation showed that our methods could significantly improve the accuracy of item recommendation, which verified the importance of considering interest-forgetting in recommendations.

The short-term reconsumption behaviors, i.e. “reconsume” the near past, account for a large proportion of people’s activities every day and everywhere. In this paper, we firstly derived four generic features which influence people’s short-term reconsumption behaviors. These features were extracted with respect to different roles in the process of reconsumption behaviors, i.e. users, items and interactions. Then, we brought forward two fast algorithms with the linear and the quadratic kernels to predict whether a user will perform a short-term reconsumption at a specific time given the context. The experimental results show that our proposed algorithms are more accurate in the prediction tasks compared with the baselines. Meanwhile, the time complexity of online prediction of our algorithms is O(1), which enables fast prediction in real-world scenarios. The prediction contributes to more intelligent decision-making, e.g. potential revisited customer identification, personalized recommendation, and information re-finding.

In recent years, community structure has attracted increasing attention in social network analysis. However, performances of multifarious approaches to community detection are seldom evaluated in a suite of systematic measurements. Furthermore, we can hardly find works which reveal diverse features based on the detected community structure. In this paper, we build a tool called CoDEM to make both quality evaluations of community detection and an in-depth mining for pivotal nodes inside communities. This tool integrates several effective approaches to community detection, establishes an overall evaluation system and gets the multi-dimensional ranking for the local importance of nodes. Moreover, the tool is built with a friendly user interface.

Music recommendation plays a key role in our daily lives as well as in the multimedia industry. This paper adapts the memory forgetting curve to model the human interest-forgetting curve for music recommendations based on the observation of recency effects in people's listening to music. Two music recommendation methods are proposed using this model with respect to the sequence-based and the IFC-based transition probabilities, respectively. We also bring forward a learning method to approximate the global optimal or personalized interest-forgetting speed(s). The experimental results show that our methods can significantly improve the accuracy in music recommendations. Meanwhile, the IFC-based method outperforms the sequence-based method when recommendation list is short at each time.

In this demonstration, we proposed a hot plugging tool for the real-time motion-based system control, which is more portable and application-independent than the existing commercial motion-based sensing devices such as Kinect, Wii and PlayStation Move. This tool captures and recognizes people's real-time motions through the built-in camera of PCs, mobile phones or tablets, and automatically executes the system events which have been mapped with people's customized body motion, e.g., the head and the fist. The tool relieves people from the conventional ways to play games and use applications, and enables them to customize their preferred ways to control the systems.

Music stretching resistance (MSR) is a fresh but important concept in audio signal processing, which characterizes the ability of a music piece to be stretched in time (compressed or elongated) without objectionable perceptual artifacts. It has the potential to be highly demanded in various multimedia applications like music resizing, audio editing and multimedia integration, but there is almost no prior knowledge about this property of music in literature. In this letter, the task of MSR is formulated for the first time, and an MSR classification method that employs metric learning on audio features and genres is also proposed. It attempts to automate what human acceptable time-stretching rate range of music should be. The proposed method outperforms the reference classification methods in accuracy in the comparative experiments.