11 research outputs found
Comprehensive review:Computational modelling of Schizophrenia
Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence.Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated
Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests
Collaborative Filtering is largely applied to personalize item recommendation
but its performance is affected by the sparsity of rating data. In order to
address this issue, recent systems have been developed to improve
recommendation by extracting latent factors from the rating matrices, or by
exploiting trust relations established among users in social networks. In this
work, we are interested in evaluating whether other sources of preference
information than ratings and social ties can be used to improve recommendation
performance. Specifically, we aim at testing whether the integration of
frequently co-occurring interests in information search logs can improve
recommendation performance in User-to-User Collaborative Filtering (U2UCF). For
this purpose, we propose the Extended Category-based Collaborative Filtering
(ECCF) recommender, which enriches category-based user profiles derived from
the analysis of rating behavior with data categories that are frequently
searched together by people in search sessions. We test our model using a big
rating dataset and a log of a largely used search engine to extract the
co-occurrence of interests. The experiments show that ECCF outperforms U2UCF
and category-based collaborative recommendation in accuracy, MRR, diversity of
recommendations and user coverage. Moreover, it outperforms the SVD++ Matrix
Factorization algorithm in accuracy and diversity of recommendation lists
Unveiling the features of successful eBay smartphone sellers
The present study adopts a data mining approach based on support vector machines (SVM) for modeling the number of sales of smartphone devices by eBay sellers. The data-based sensitivity analysis was adopted for extracting meaningful knowledge translated into the relevance of each input feature for the model. Such approach allowed unveiling that the number of items the seller also has on auctions, the price and the variety of products the seller offers are the three features that influence most the number of sales, in a total of almost 25%, surpassing the relevance of the features related to customers' feedback