207,392 research outputs found

    A Bayesian Approach toward Active Learning for Collaborative Filtering

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    Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the performance of collaborative filtering depends on the number of rated examples given by the active user. The more the number of rated examples given by the active user, the more accurate the predicted ratings will be. Active learning provides an effective way to acquire the most informative rated examples from active users. Previous work on active learning for collaborative filtering only considers the expected loss function based on the estimated model, which can be misleading when the estimated model is inaccurate. This paper takes one step further by taking into account of the posterior distribution of the estimated model, which results in more robust active learning algorithm. Empirical studies with datasets of movie ratings show that when the number of ratings from the active user is restricted to be small, active learning methods only based on the estimated model don't perform well while the active learning method using the model distribution achieves substantially better performance.Comment: Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004

    Efficient Model Learning for Human-Robot Collaborative Tasks

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    We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human. The learning takes place completely automatically, without any human intervention. First, we describe the clustering of demonstrated action sequences into different human types using an unsupervised learning algorithm. These demonstrated sequences are also used by the robot to learn a reward function that is representative for each type, through the employment of an inverse reinforcement learning algorithm. The learned model is then used as part of a Mixed Observability Markov Decision Process formulation, wherein the human type is a partially observable variable. With this framework, we can infer, either offline or online, the human type of a new user that was not included in the training set, and can compute a policy for the robot that will be aligned to the preference of this new user and will be robust to deviations of the human actions from prior demonstrations. Finally we validate the approach using data collected in human subject experiments, and conduct proof-of-concept demonstrations in which a person performs a collaborative task with a small industrial robot

    Gender Differences in Preference for Learning Environment Among Aviation Education Students

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    This study investigated whether differences existed between sex, male and female, for the preference of three different syllabi describing three different learning environments. Learning environments consisted of collaborative, and individual, with the individual sub-divided into competitive, and individual while co-varying participants for credit hours. 264 surveys were administered to students in freshman, sophomore, junior, and senior classes in order to collect preference, and demographic data. The surveys were presented as three fictional syllabi differing only in class grading format, and a paragraph on the instructional philosophy of the professor. Instructional philosophies described the proposed environment of the class by enforcing the individual, competitive, or collaborative instructional methods. According to recent literature, women were predicted to prefer collaborative classroom environments to individual/competitive classroom environments and males were predicted to prefer competitive/individual over collaborative classroom environments. Limitations for the present study were discussed as well as suggestions for future research

    COLLABORATIVE LEARNING IN THE 21ST CENTURY TEACHING AND LEARNING LANDSCAPE: EFFECTS TO STUDENTS’ COGNITIVE, AFFECTIVE AND PSYCHOMOTOR DIMENSIONS

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    Collaborative Learning (CL) is an approach to teaching and learning that involves a group of learners working together. The study aims to assess collaborative learning effects in the 21st century teaching-learning landscape, particularly on the students' cognitive, affective and psychomotor dimensions. This study utilized the descriptive-evaluative method of research with a validated questionnaire as the primary data gathering instrument. Results showed that females dominate male students as to their number in first and second-year levels. The majority of the respondents aged 20 and below have participated in various collaborative activities and assignments in and out of the class with a typical size of 5 to 7 group members. Collaborative learning found to have a very high effect on Students' Academic Learning (cognitive), Collaborative Skills (affective) and least on Skills development (psychomotor). The researchers recommend that male students be engaged in group dynamics and learning activities; it must involve teenagers and suffice their activities' preference. Activities that demand greater participation, drills and exercises, and thinking activities must allow the students to develop their cognitive, affective, and psychomotor dimensions for holistic learning

    Hierarchical Attention Network for Visually-aware Food Recommendation

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    Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

    Effects of Active Learning Variants on Student Performance and Learning Perceptions

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    This paper aims to examine the relative impacts of three different models of learning (collaborative learning, traditional lecturing and process-oriented guided inquiry learning [POGIL]) on student performance and learning perceptions. In a controlled case study, we measured the learning outcomes of 57 undergraduates in a chemistry course taught by the different learning modules, using quizzes and exams as performance measures. In one academic quarter, the collaborative learning method was used exclusively whereas all three models were used subsequently in a second quarter by dividing up lectures into 4 different modules. Student quiz and exam outcomes indicated significant difference between collaborative learning and traditional lecturing (P = 0.01) but not within the active learning variants or POGIL versus traditional lecturing (P \u3e 0.05), suggesting students performed best on content taught by collaborative learning. When prompted to pick a learning module, 67% of the students chose collaborative learning but not POGIL, indicative of student preference for one active learning variant over the other. However, student engagement and higher-order thinking appeared to be higher under the POGIL module though both skills were also evident during the collaborative learning period. Based on the outcome of the present study, it is recommended that purely inquiry-based lectures should employ short-burst intermittent lecturing to overcome student resistance and negative perceptions
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