Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation

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).

<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

Demographics of amblyopic patients and controls at the baseline.

<p>Demographics of amblyopic patients and controls at the baseline.</p

Difference between amblyopic eyes and control hyperopic eyes.

<p>Difference between amblyopic eyes and control hyperopic eyes.</p

Choroidal area of the amblyopic and fellow eyes.

<p>Choroidal area of the amblyopic and fellow eyes.</p

Demographic information of the amblyopic patients and controls.

<p>Demographic information of the amblyopic patients and controls.</p

Comparison of the choroidal area between the control hyperopic, emmetropic, and myopic eyes.

<p>Comparison of the choroidal area between the control hyperopic, emmetropic, and myopic eyes.</p

Enhanced depth spectral-domain optical coherence tomographic (EDI-OCT) images of the choroid of an amblyopic eye and a control eye.

<p>(A) Representative image of the amblyopic eye. The amblyopic eye has large luminal area. (B) Representative image of the control hyperopic eye. The control hyperopic eye has small luminal area. The choroidal area measured was 1500 μm wide with the margins 750 μm nasal and 750 μm temporal to the fovea. Vertically, the area extended from the retinal pigment epithelium to the chorioscleral border (yellow line). The measured area of the choroid is demarcated (Top). The image is converted to a binary image by the Niblack method of ImageJ (Middle). The dark area which is luminal area is traced by the red line (Bottom).</p