With the incredibly growing amount of multimedia data shared on the social media platforms, recommender systems have become an important necessity to ease users' burden on the information overload. ...In such a scenario, extensive amount of heterogeneous information such as tags, image content, in addition to the user-to-item preferences, is extremely valuable for making effective recommendations. In this paper, we explore a novel hybrid algorithm termed {\em STM}, for image recommendation. STM jointly considers the problem of image content analysis with the users' preferences on the basis of sparse representation. STM is able to tackle the challenges of highly sparse user feedbacks and cold-start problmes in the social network scenario. In addition, our model is based on the classical probabilistic matrix factorization and can be easily extended to incorporate other useful information such as the social relationships. We evaluate our approach with a newly collected 0.3 million social image data set from Flickr. The experimental results demonstrate that sparse topic modeling of the image content leads to more effective recommendations, , with a significant performance gain over the state-of-the-art alternatives.
In the realm of Virtual Reality (VR), the exploration of players' modes of free movement has persistently been a pivotal research focus. This study introduces a novel locomotion approach, ...specifically employing gaze-directed instantaneous destination selection, aiming to enhance the current methods of movement in VR games and elevate the overall user experience. Through the creation of corresponding game scenarios, we assess the instantaneous movement performance in terms of efficiency, precision, and comfort for both gaze-directed and controller-directed destination selection. Statistical analyses reveal that, in terms of efficiency, gaze-directed instantaneous destination selection outperforms controller-directed movement. However, in the evaluation of comfort, controller-directed movement surpasses gaze-directed instantaneous destination. The findings of this research not only present a viable alternative in movement methodology but also underscore the necessity of striking a balance between efficiency and comfort in VR game design to deliver an enhanced gaming experience.
Managing and maintaining fishing ports is crucial for fishing activities. With the lack of budget and labor force, some fishing ports face the brink of being shut down. A first choice for demolishing ...would be fishing ports with low utilization, but this does not mean the port is totally useless. When severe weather condition, i.e., typhoons, is in the proximity of the coastal line, offshore fishing vessels will return to dock in ports for safety. When major ports are fully docked, some of the minor ports or less active ports will be chosen as the haven. These ports are seldom used and lowly utilized, but should still be maintained for safety issues as they serve as a sanctuary for extreme weather conditions. This research uses big data aggregation approach to identify haven ports by processing fishing vessel states using collected voy-age data recorder (VDR) records and correlates this information with the typhoon information data source.
Delta-SimRank computing on MapReduce Cao, Liangliang; Cho, Brian; Kim, Hyun Duk ...
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications,
08/2012
Conference Proceeding
Based on the intuition that "two objects are similar if they are related to similar objects", SimRank (proposed by Jeh and Widom in 2002) has become a famous measure to compare the similarity between ...two nodes using network structure. Although SimRank is applicable to a wide range of areas such as social networks, citation networks, link prediction, etc., it suffers from heavy computational complexity and space requirements. Most existing efforts to accelerate SimRank computation work only for static graphs and on single machines. This paper considers the problem of computing SimRank efficiently in a distributed system while handling dynamic networks which grow with time. We first consider an abstract model called Harmonic Field on Node-pair Graph. We use this model to derive SimRank and the proposed Delta-SimRank, which is demonstrated to fit the nature of distributed computing and can be efficiently implemented using Google's MapReduce paradigm. Delta-SimRank can effectively reduce the computational cost and can also benefit the applications with non-static network structures. Our experimental results on four real world networks show that Delta-SimRank is much more efficient than the distributed SimRank algorithm, and leads to up to 30 times speed-up in the best case1.
In the field of machine learning and pattern recognition, an alternative to conventional classification is hierarchical classification that exploits hierarchical relations between concepts of ...interest. To the best of our knowledge, all hierarchical classification methods in the literature are designed to reduce computation complexity without sacrificing too much on accuracy performance. In this work on image classification, we first propose a hierarchical image feature extraction that extracts image feature based on the location of current node in hierarchy to fit the images under current node and to better distinguish its subclasses. As far as we know, such node-dependent feature extraction has not been considered in the literature. Contrary to former hierarchical classification methods that only consider local structure of the hierarchy, we propose a novel cross-level hierarchical classification method that utilizes both global and local concept structures throughout the entire path decision-making process. Our experimental result on two datasets shows that the proposed hierarchical feature extraction combined with our novel hierarchical classification achieves better accuracy performance than conventional non-hierarchical classification methods, and hence conventional hierarchical methods as well.
In this dissertation, we study the problem of social media recommendations with a heavy emphasis on exploiting social, content and contextual information. The problem of recommendation analysis and ...collaborative filtering has been widely studied in the literature because of its numerous applications to a wide variety of scenarios. Many social media sites such as Flickr or YouTube contain multimedia objects, which occur in the context of an extensive amount of content information such as tags, image content, in addition to the user preferences for the different objects. Thus, there is a plethora of heterogeneous, content, linkage and preference information in a social media network, which can be used in order to make effective recommendations in such networks. In this dissertation, we will study the problem of making recommendations in such complex multimedia networks with the use of such information. While our approach is developed and evaluated for the case of the Flickr image network, the broad principles are applicable to any kind of multimedia network such as a music or video site. To ensure the efficiency and scalability, we further extend our approach to incorporate the latent factor model so that our approach can be very useful for making personalized content recommendations in large and heterogeneous social media networks. This dissertation also studies a variety of recommendation scenarios, including context-specific recommendations which are made based on some kinds of content the user specifies, cold start recommendations that utilizes the social relations to make recommendations when a new user gets on board, and preference drifting to realize the long-term change of users' tastes. We present experimental results illustrating the effectiveness and efficiency of our approaches.
A transient
10
6
-fold concentration of double-layer counterions by a high-intensity electric field is demonstrated at the exit pole of a millimeter-sized conducting nanoporous granule that permits ...ion permeation. The phenomenon is attributed to a unique counterion screening dynamics that transforms half of the surface field into a converging one toward the ejecting pole. The resulting surface conduction flux then funnels a large upstream electro-osmotic convective counterion flux into the injecting hemisphere toward the zero-dimensional gate of the ejecting hemisphere to produce the superconcentration. As the concentrated counterion is ejected into the electroneutral bulk electrolyte, it attracts co-ions and produce a corresponding concentration of the co-ions. This mechanism is also shown to trap and concentrate co-ion microcolloids of micron sizes too (macroions) and hence has potential application in bead-based molecular assays.
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