CRACS - Indexed Articles in Conferences

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Browsing CRACS - Indexed Articles in Conferences by Author "Alípio Jorge"
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    Accelerating Recommender Systems using GPUs
    ( 2015) André Valente Rodrigues ; Alípio Jorge ; Inês Dutra
    We describe GPU implementations of the matrix recommender algorithms CCD++ and ALS. We compare the processing time and predictive ability of the GPU implementations with existing multi- core versions of the same algorithms. Results on the GPU are better than the results of the multi- core versions (maximum speedup of 14.8).
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    Long term goal oriented recommender systems
    ( 2015) José Paulo Leal ; Alípio Jorge ; AmirHossein Nabizadeh ; 5125 ; 4981 ; 6083
    The main goal of recommender systems is to assist users in finding items of their interest in very large collections. The use of good automatic recommendation promotes customer loyalty and user satisfaction because it helps users to attain their goals. Current methods focus on the immediate value of recommendations and are evaluated as such. This is insufficient for long term goals, either defined by users or by platform managers. This is of interest in recommending learning resources to learn a target concept, and also when a company is organizing a campaign to lead users to buy certain products or moving to a different customer segment. Therefore, we believe that it would be useful to develop recommendation algorithms that promote the goals of users and platform managers (e.g. e-shop manager, e-learning tutor, ministry of culture promotor). Accordingly, we must define appropriate evaluation methodologies and demonstrate the concept on practical cases.
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    Long term goal oriented recommender systems
    ( 2015) AmirHossein Nabizadeh ; Alípio Jorge ; José Paulo Leal
    The main goal of recommender systems is to assist users in finding items of their interest in very large collections. The use of good automatic recommendation promotes customer loyalty and user satisfaction because it helps users to attain their goals. Current methods focus on the immediate value of recommendations and are evaluated as such. This is insufficient for long term goals, either defined by users or by platform managers. This is of interest in recommending learning resources to learn a target concept, and also when a company is organizing a campaign to lead users to buy certain products or moving to a different customer segment. Therefore, we believe that it would be useful to develop recommendation algorithms that promote the goals of users and platform managers (e.g. e-shop manager, e-learning tutor, ministry of culture promotor). Accordingly, we must define appropriate evaluation methodologies and demonstrate the concept on practical cases.
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    RUTICO: Recommending Successful Learning Paths Under Time Constraints
    ( 2017) José Paulo Leal ; Alípio Jorge ; AmirHossein Nabizadeh ; 5125 ; 4981 ; 6083
    Nowadays using E-learning platforms such as Intelligent Tutoring Systems (ITS) that support users to learn subjects are quite common. Despite the availability and the advantages of these systems, they ignore the learners' time limitation for learning a subject. In this paper we propose RUTICO, that recommends successful learning paths with respect to a learner's knowledge background and under a time constraint. RUTICO, which is an example of Long Term goal Recommender Systems (LTRS), a.er locating a learner in the course graph, it utilizes a Depth-first search (DFS) algorithm to find all possible paths for a learner given a time restriction. RUTICO also estimates learning time and score for the paths and finally, it recommends a path with the maximum score that satisfies the learner time restriction. In order to evaluate the ability of RUTICO in estimating time and score for paths, we used the Mean Absolute Error and Error. Our results show that we are able to generate a learning path that maximizes a learner's score under a time restriction. © 2017 ACM.
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    A study of machine learning methods for detecting user interest during web sessions
    ( 2014) Alípio Jorge ; Anand,SS ; José Paulo Leal ; Dias,H
    The ability to have an automated real time detection of user interest during a web session is very appealing and can be very useful for a number of web intelligence applications. Low level interaction events associated with user interest manifestations form the basis of user interest models. However such data sets present a number of challenges from a machine learning perspective, including the level of noise in the data and class imbalance (given that the majority of content will not be of interest to a user). In this paper we evaluate a large number of machine learning techniques aimed at learning from class imbalanced data using two data sets collected from a real user study. We use the AUC, recall, precision and model complexity to compare the relative merits of these techniques and conclude that useful models with AUC above 0.8 can be obtained using a mix of sampling and cost based methods. Ensemble models can provide further accuracy but make deployment more complex. Copyright 2014 ACM.