Online social networks provide relevant information on users' opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper pr...
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Online social networks provide relevant information on users' opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper presents a knowledge-based recommendation system (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending on the monitoring results, the KBRS, based on ontologies and sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in case a depression disturbance is detected by the monitoring system. The detection of sentences with depressive and stressful content is performed through a convolutional neural network and a bidirectional long short-term memory recurrent neural networks (RNN);the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices.
Recommender systems aim to suggest lists of items that match accurately the user's preferences. In the last years it has been argued that the diversity of the recommendations also plays an important role in the ov...
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Recommender systems aim to suggest lists of items that match accurately the user's preferences. In the last years it has been argued that the diversity of the recommendations also plays an important role in the overall satisfaction of the user. Increasing the diversity of the suggestions may be beneficial both for the user and for retailers. This paper provides a brief review of the most popular diversification mechanisms and it introduces two new ones (Cluster Random and Cluster Quadratic) based on the semantic clustering of the domain objects. It also shows how the level of diversification may be dynamically adapted to the variety in the preferences of the user. A thorough evaluation of the diversification mechanisms on a Tourism recommender has been performed, reaching the conclusion that the new Cluster Quadratic diversification method achieves very competitive levels of precision and recall, while keeping an acceptable computational cost.
Most real-world databases have at least some missing data. Today, users of such databases are "on their own" in terms of how they manage this incompleteness. In this paper, we propose the general concept of ...
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Most real-world databases have at least some missing data. Today, users of such databases are "on their own" in terms of how they manage this incompleteness. In this paper, we propose the general concept of partial information policy (PIP) operator to handle incompleteness in relational databases. PIP operators build upon preference frameworks for incomplete information, but accommodate different types of incomplete data (e.g., a value exists but is not known;a value does not exist;a value may or may not exist). Different users in the real world have different ways in which they want to handle incompleteness-PIP operators allow them to specify a policy that matches their attitude to risk and their knowledge of the application and how the data was collected. We propose index structures for efficiently evaluating PIP operators and experimentally assess their effectiveness on a real-world airline data set. We also study how relational algebra operators and PIP operators interact with one another.
In this paper, we present a multiagent system to support patients in search of healthcare services in an e-health scenario. The proposed system is HL7-aware in that it represents both patient and service information a...
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In this paper, we present a multiagent system to support patients in search of healthcare services in an e-health scenario. The proposed system is HL7-aware in that it represents both patient and service information according to the directives of HL7, the information management standard adopted in medical context. Our system builds a profile for each patient and uses it to detect Healthcare Service Providers delivering e-health services potentially capable of satisfying his needs. In order to handle this search it can exploit three different algorithms: the first, called PPB, uses only information stored in the patient profile;the second, called DS-PPB, considers both information stored in the patient profile and similarities among the e-health services delivered by the involved providers;the third, called AB, relies on A*, a popular search algorithm in Artificial Intelligence. Our system builds also a social network of patients;once a patient submits a query and retrieves a set of services relevant to him, our system applies a spreading activation technique on this social network to find other patients who may benefit from these services.
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