18,876 research outputs found
Leveraging Mobile App Classification and User Context Information for Improving Recommendation Systems
Mobile apps play a significant role in current online environments where there is an overwhelming supply of information. Although mobile apps are part of our daily routine, searching and finding mobile apps is becoming a nontrivial task due to the current volume, velocity and variety of information. Therefore, app recommender systems provide users’ desired apps based on their preferences. However, current recommender systems and their underlying techniques are limited in effectively leveraging app classification schemes and context information. In this thesis, I attempt to address this gap by proposing a text analytics framework for mobile app recommendation by leveraging an app classification scheme that incorporates the needs of users as well as the complexity of the user-item-context information in mobile app usage pattern. In this recommendation framework, I adopt and empirically test an app classification scheme based on textual information about mobile apps using data from Google Play store. In addition, I demonstrate how context information such as user social media status can be matched with app classification categories using tree-based and rule-based prediction algorithms. Methodology wise, my research attempts to show the feasibility of textual data analysis in profiling apps based on app descriptions and other structured attributes, as well as explore mechanisms for matching user preferences and context information with app usage categories. Practically, the proposed text analytics framework can allow app developers reach a wider usage base through better understanding of user motivation and context information
Retrospective Interference Alignment for Two-Cell Uplink MIMO Cellular Networks with Delayed CSIT
In this paper, we propose a new retrospective interference alignment for
two-cell multiple-input multiple-output (MIMO) interfering multiple access
channels (IMAC) with the delayed channel state information at the transmitters
(CSIT). It is shown that having delayed CSIT can strictly increase the sum-DoF
compared to the case of no CSIT. The key idea is to align multiple interfering
signals from adjacent cells onto a small dimensional subspace over time by
fully exploiting the previously received signals as side information with
outdated CSIT in a distributed manner. Remarkably, we show that the
retrospective interference alignment can achieve the optimal sum-DoF in the
context of two-cell two-user scenario by providing a new outer bound.Comment: 7 pages, 2 figures, to appear in IEEE ICC 201
Direct Measure of Giant Magnetocaloric Entropy Contributions in Ni-Mn-In
Off-stoichiometric alloys based on Ni 2 MnIn have drawn attention due to the
coupled first order magnetic and structural transformations, and the large
magnetocaloric entropy associated with the transformations. Here we describe
calorimetric and magnetic studies of four compositions. The results provide a
direct measure of entropy changes contributions including at the first-order
phase transitions, and thereby a determination of the maximum field-induced
entropy change corresponding to the giant magnetocaloric effect. We find a
large excess entropy change, attributed to magneto-elastic coupling, but only
in compositions with no ferromagnetic order in the high-temperature austenite
phase. Furthermore, a molecular field model corresponding to antiferromagnetism
of the low-temperature phases is in good agreement, and nearly independent of
composition, despite significant differences in overall magnetic response of
these materials
Online Maximum k-Coverage
We study an online model for the maximum k-vertex-coverage problem, where given a graph G = (V,E) and an integer k, we ask for a subset A ⊆ V, such that |A | = k and the number of edges covered by A is maximized. In our model, at each step i, a new vertex vi is revealed, and we have to decide whether we will keep it or discard it. At any time of the process, only k vertices can be kept in memory; if at some point the current solution already contains k vertices, any inclusion of any new vertex in the solution must entail the irremediable deletion of one vertex of the current solution (a vertex not kept when revealed is irremediably deleted). We propose algorithms for several natural classes of graphs (mainly regular and bipartite), improving on an easy 1/2-competitive ratio. We next settle a set-version of the problem, called maximum k-(set)-coverage problem. For this problem we present an algorithm that improves upon former results for the same model for small and moderate values of k
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