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Context-aware recommender systems (CARS) are specially designed to take into account the contextual conditions under which a user experiences an item, with the goal of generating improved recommendations. A known difficulty when constructing recommender systems is data sparseness, which reduces the effectiveness of collaborative filtering algorithms. While using contextual information provides fine-grained signals for the recommendation process, it makes the data even sparser and increases the computational complexity. In this paper, we present a method for making context-aware recommendations, which is less sensitive to data sparseness. The proposed method exploits the transitivity of the interactions between users and items on the user-item graph to augment the direct interactions, thus reducing the negative effect of sparse data. Based on graph transitivity we introduce a new graph-based association measure that we use as a measure of similarity between two users or two items in nearest neighbor recommendation methods. This combination of graph-based similarity measure with nearest neighbor methods allows considering more contextual conditions at a lower risk of being affected by data sparseness caused by additional contextual dimensions. We experimentally evaluated the proposed method on three contextually-tagged data sets. The results show that our method outperforms several baselines and state-of-the-art context-aware recommendation methods.

Unmanned aerial vehicles play a very important role in different technical problems. They can take photographs of the area, track forest fires, control offenses and be useful in another fields. Nowadays tethered high-altitude platforms have a particular interest. They have a number of advantages over autonomous devices. For example, they can work for a sufficiently long time and do not depend on the limited resources of batteries. This allows them to be used as transmitters for mobile phones and for other purposes where it is impossible to use autonomous unmanned platforms. In addition, they can carry a much higher load and have a higher power. However, there are some problems connected with navigation of the platform. The wind can change the location of the platform, which should be corrected. The coordinates of unmanned aerial vehicles are usually found using GPS or GLONASS systems. Unfortunately, the signal can be quite weak in some areas, so it is necessary to take the reserve navigation system, which will be connected with beacons located on the ground. In this work we present the idea of system of navigation of the tethered platform, which takes into account its main features and can give us the opportunity to stabilize the location of the vehicle. We give the main formulaes for the coordinates and estimate typical mistakes while measuring them.

Collaborative filtering is a technique widely used in recommender systems. Based on behaviors of users with similar taste, the technique can predict and recommend products the current user is likely interested in, thus alleviates the information overload problem for Internet users. The most popular collaborative filtering approach is based on the similarity between users, or between products. The quality of similarity measure, therefore, has a large impact on the recommendation accuracy. In this paper, we propose a new similarity measure based on graph models. The similarity between two users (or symmetrically, two products) is computed from connections on a graph with vertices being users and products. The computed similarity measure is then used with the k - nearest neighbor algorithm to generate predictions. Empirical results on real movie datasets show that the proposed method significantly outperforms both collaborative filtering with traditional similarity measures and pure graph-based collaborative filtering.

Collaborative filtering is a technique to predict userspsila interests for items by exploiting the behavior patterns of a group of users with similar preferences. This technique has been widely used for recommender systems and has a number of successful applications in E-commerce. In practice, a major challenge when applying collaborative filtering is that a typical user provides ratings for just a small number of items, thus the amount of training data is sparse with respect to the size of the domain. In this paper, we present a method to address this problem. Our method formulates the collaborative filtering problem in a multi-task learning framework by treating each user rating prediction as a classification problem and solving multiple classification problems together. By doing this, the method allows sharing information among different classifiers and thus reduces the effect of data sparsity.

System Security means protecting confidentiality and integrity of input or output data to as well as the safety of the system. To meet stated security requirements, designers will need to add some special functions into the system. However, in several cases including high-throughput requirement, limited resource or trade-off between risks and costs, adding security functions should be taken into comprehensive consideration. This paper presents a method to enhance safety and update speed of the partially reconfigurable embedded systems based on FPGA is updated remotely via the Internet or from external storage devices such as a Compact Flash (CF) memory card.