24 research outputs found

    Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation

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    In recent years, vast amounts of user-generated data have being created on Location-Based Social Networks (LBSNs) such as Yelp and Foursquare. Making effective personalised venue suggestions to users based on their preferences and surrounding context is a challenging task. Context-Aware Venue Recommendation (CAVR) is an emerging topic that has gained a lot of attention from researchers, where context can be the user's current location for example. Matrix Factorisation (MF) is one of the most popular collaborative filtering-based techniques, which can be used to predict a user's rating on venues by exploiting explicit feedback (e.g. users' ratings on venues). However, such explicit feedback may not be available, particularly for inactive users, while implicit feedback is easier to obtain from LBSNs as it does not require the users to explicitly express their satisfaction with the venues. In addition, the MF-based approaches usually suffer from the sparsity problem where users/venues have very few rating, hindering the prediction accuracy. Although previous works on user-venue rating prediction have proposed to alleviate the sparsity problem by leveraging user-generated data such as social information from LBSNs, research that investigates the usefulness of Deep Neural Network algorithms (DNN) in alleviating the sparsity problem for CAVR remains untouched or partially studied

    Rate of change in subfoveal choroidal thickness (CT).

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    <p>A: Changes of the subfoveal CT of amblyopic eyes in patients with anisohypermetropic amblyopia. The thicker choroids became thinner and thinner choroids became thicker. There was a negative correlation between the rate of change in the subfoveal choroidal thickness and the baseline subfoveal choroidal thickness. (<i>r</i> = -0.59, <i>P</i> = 0.003; Pearson’s correlation coefficient). B: Changes of the subfoveal CT of the fellow eyes in patients with anisohypermetropic amblyopia. The thicker choroid became thinner and thinner choroid became thicker. There was a negative correlation between the rate of change in the subfoveal choroidal thickness and the baseline subfoveal choroidal thickness. (<i>r</i> = -0.48, <i>P</i> = 0.02; Pearson’s correlation coefficient). C: Changes of the subfoveal CT of control eyes. There was no correlation between the rate of change in the subfoveal choroidal thickness and the baseline subfoveal choroidal thickness. (<i>r</i> = -0.15, <i>P</i> = 0.49; Pearson’s correlation coefficient).</p

    Correlations between VWF antigen and parameters (BCVA, CRT, and CCT) at 1 month after anti-VEGF treatment.

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    In each graph, the horizontal axis presents the VWF antigen (%). BCVA, best-corrected visual acuity (LogMAR unit); CRT, central retinal thickness; CCT, central choroidal thickness; RVO, retinal vein occlusion.</p

    Alterations of VWF antigen after anti-VEGF treatment.

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    Each of the values above the graph presents the median with the interquartile range. *< 0.05, **< 0.01. RVO, retinal vein occlusion.</p

    The correlation between VWF antigen and parameters (BCVA, CRT, and CCT) at baseline.

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    In each graph, the horizontal axis presents the VWF antigen (%). BCVA, best-corrected visual acuity (LogMAR unit); CRT, central retinal thickness; CCT, central choroidal thickness; RVO, retinal vein occlusion.</p

    Alterations of VWF antigen in patients with branch RVO according to the administered drug.

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    Each of the values above the graph presents the median with the interquartile range. *< 0.05, **< 0.01. RVO, retinal vein occlusion.</p
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