An architecture for life-long user modelling

In this paper, we propose a united architecture for the creation of life-long user profiles. Our architecture combines different steps required for a user prole, including feature extraction and representation, reasoning, recommendation and presentation. We discuss various issues that arise in the context of life-long profiling

Intravitreal vs. subtenon triamcinolone acetonide for the treatment of diabetic cystoid macular edema

<p>Abstract</p> <p>Background</p> <p>To assess the efficacy of the intravitreal (IVT) injection of Triamcinolone Acetonide (TA) as compared to posterior subtenon (SBT) capsule injection for the treatment of cystoid diabetic macular edema.</p> <p>Methods</p> <p>Fourteen patients with type II diabetes mellitus and on insulin treatment, presenting diffuse cystoid macular edema were recruited. Before TA injection all focal lakes were treated by laser photocoagulation. In the same patients one eye was assigned to 4 mg IVT injection of TA and the fellow eye was then treated with 40 mg SBT injection of TA. Before and one, three and six months after treatment we measured visual acuity with ETDRS chart as well as thickness of the macula with optical coherence tomography (OCT) and intraocular pressure (IOP).</p> <p>Results</p> <p>The eyes treated with an IVT injection displayed significant improvement in visual acuity, both after one (0.491 ± 0.070; p < 0.001) and three months (0.500 ± 0.089; p < 0.001) of treatment. Significant improvement was displayed also in eyes treated with an SBT injection, again after one (0.455 ± 0.069; p < 0.001) and three months (0.427 ± 0.065; p < 0.001). The difference between an IVT injection (0.809 ± 0.083) and SBT injection (0.460 ± 0.072) becomes significant six months after the treatment (p < 0.001).</p> <p>Macular thickness of the eyes treated with IVT injection was significantly reduced both after one (222.7 ± 13.4 μm; p < 0.001) and after three months (228.1 ± 10.6 μm; p < 0.001) of treatment. The eyes treated with SBT injection displayed significant improvement after one (220.1 ± 15.1 μm; p < 0.001) and after three months (231.3 ± 10.9 μm; p < 0.001). The difference between the eyes treated with IVT injection (385.2 ± 11.3 μm) and those treated with SBT injection (235.4 ± 8.7 μm) becomes significant six months after the treatment (p < 0.001).</p> <p>Intraocular pressure of the eyes treated with IVT injection significantly increased after one month (17.7 ± 1.1 mm/Hg; p < 0.020), three (18.2 ± 1.2 mm/Hg; p < 0.003) and six month (18.1 ± 1.3 mm/Hg; p < 0.007) when compared to baseline value (16.1 ± 1.402 mm/Hg). In the SBT injection eyes we didn't display a significant increase of intraocular pressure after one (16.4 ± 1.2 mm/Hg; p < 0.450), three (16.3 ± 1.1 mm/Hg; p < 0.630) and six months (16.2 ± 1.1 mm/Hg; p < 0.720) when compared to baseline value (16.2 ± 1.3 mm/Hg).</p> <p>Conclusion</p> <p>The parabulbar subtenon approach can be considered a valid alternative to the intravitreal injection.</p> <p>Trial registration</p> <p>Current Controlled Trials <b>ISRCTN67086909</b></p

Role of emotional features in collaborative recommendation

The aim of this poster is to investigate the role of emotion in the collaborative filtering task. For this purpose, a kernel-based collaborative recommendation technique is used. The experiment is conducted on two MovieLens data sets. The emotional features are extracted from the movie reviews and plot summaries. The results show that emotional features are capable of enhancing recommendation effectiveness

Handling data sparsity in collaborative filtering using emotion and semantic based features

Collaborative filtering (CF) aims to recommend items based on prior user interaction. Despite their success, CF techniques do not handle data sparsity well, especially in the case of the cold start problem where there is no past rating for an item. In this paper, we provide a framework, which is able to tackle such issues by considering item-related emotions and semantic data. In order to predict the rating of an item for a given user, this framework relies on an extension of Latent Dirichlet Allocation, and on gradient boosted trees for the final prediction. We apply this framework to movie recommendation and consider two emotion spaces extracted from the movie plot summary and the reviews, and three semantic spaces: actor, director, and genre. Experiments with the 100K and 1M MovieLens datasets show that including emotion and semantic information significantly improves the accuracy of prediction and improves upon the state-of-the-art CF techniques. We also analyse the importance of each feature space and describe some uncovered latent groups

Exploring term temporality for pseudo-relevance feedback

As digital collections expand, the importance of the temporal aspect of information has become increasingly apparent. The aim of this paper is to investigate the effect of using long-term temporal profiles of terms in information retrieval by enhancing the term selection process of pseudo-relevance feedback (PRF). For this purpose, two temporal PRF approaches were introduced considering only temporal aspect and temporal along with textual aspect. Experiments used the AP88-89 and WSJ87-92 test collections with TREC Ad-Hoc Topics 51-100. Term temporal profiles are extracted from the Google Books n-grams dataset. The results show that the long-term temporal aspects of terms are capable of enhancing retrieval effectiveness

Improving search results with prior similar queries

This paper describes a novel approach to re-ranking search engine result pages (SERP): Its fundamental principle is to re-rank results to a given query, based on exploiting evidence gathered from past similar search queries. Our approach is inspired by collaborative filtering, with the main challenge being to find the set of similar queries, while also taking efficiency into account. In particular, our approach aims to address this challenge by proposing a combination of a similarity graph and a locality sensitive hashing scheme. We construct a set of features from our similarity graph and build a prediction model using the Hoeffding decision tree algorithm. We have evaluated the effectiveness of our model in terms of P@1, MAP@10, and nDCG@10, using the Yandex Data Challenge data set. We have compared the performance of our model against two baselines, namely, the Yandex initial ranking and the decision tree model learnt on the same set of features when extracted based on query repetition (i.e. excluding the evidence of similar queries in our approach). Our results reveal that the proposed approach consistently and (statistically) significantly outperforms both baselines. © 2016 ACM

Screening and treatments using telemedicine in retinopathy of prematurity

Aristomenis Thanos,1 Yoshihiro Yonekawa,1,2 Bozho Todorich,1 Darius M Moshfeghi,3 Michael T Trese1 1Associated Retinal Consultants, William Beaumont Hospital, Royal Oak, MI, 2Retina Service, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, 3Byers Eye Institute, Horngren Family Vitreoretinal Center, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA, USA Abstract: Several studies have validated the role of telemedicine as a new powerful screening and diagnostic tool for retinal disorders, such as diabetic retinopathy and retinopathy of prematurity. With regard to retinopathy of prematurity, bedside examination with binocular indirect ophthalmoscopy has been the gold standard technique for screening, yet with several limitations. Herein, we review the current evidence that supports the role of telemedicine for the screening of infants with retinopathy of prematurity. Keywords: retinopathy of prematurity, screening, telemedicin

Assessment of nasopalatine canal anatomic variations using cone beam computed tomography in a group of Iranian population

Background: With regards to the increasing use of implants in the field of dentistry, the recognition of critical landmarks is essential. Nasopalatine canal (NPC) is one of these important indices, which due to high esthetic expectations in premaxilla, should be precisely evaluated before surgery. Objectives: This study aimed to evaluate the morphological and anatomical variations of the NPC. Materials and Methods: A total of 326 individual cone beam computed tomography (CBCT) images were analyzed in sagittal, coronal, and axial planes in order to evaluate the dimensions, morphology and anatomic features of the NPC. The canal shape, length, and curvature, incisive and Stenson’s foramina (SF) dimensions, and the number of openings on both sides of the canal were assessed. The correlation of age, gender, and dental status with all considered parameters were analyzed. Results: The most dominant shape of theNPCwas cylindrical (65.33). ThemeanNPClength was 12.85±2.63mm, which was greater in men and showed significant differences between two genders (P < 0.001). The most frequent canal anatomical variation in the coronal plane was Y-type (50). Through statistical analysis, the effect of gender on the canal length, anteroposterior dimension of SF, and mediolateral dimension of SF and incisive foramen (IF) and the number of orifices at the nasal floor was significant. Also, a significant relationship existed between dental status and curvature of the canal, anteroposterior dimension of IF and SF and furcation level of the canal. Conclusion: This study has highlighted the anatomical variations of NPC regarding its dimension, location and morphological appearance. Cylindrical was the most common shape followed by funnel-shape, hourglass, and spindle, which were the other canal shapes with less frequency, respectively. The results suggest significant relationship between NPC, and gender and dental status. The influence of age was not as significant as gender and dental status. © 2016, Tehran University of Medical Sciences and Iranian Society of Radiology

Integrating facial expressions into user profiling for the improvement of a multimodal recommender system

Over the years, recommender systems have been systematically applied in both industry and academia to assist users in dealing with information overload. One of the factors that determine the performance of a recommender system is user feedback, which has been traditionally communicated through the application of explicit and implicit feedback techniques. In this paper, we propose a novel video search interface that predicts the topical relevance of a video by analysing affective aspects of user behaviour. We, furthermore, present a method for incorporating such affective features into user profiling, to facilitate the generation of meaningful recommendations, of unseen videos. Our experiment shows that multimodal interaction feature is a promising way to improve the performance of recommendation