LIAAD - Indexed Articles in Conferences
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ItemDynamic Topic Modeling Using Social Network Analytics( 2021)Topic modeling or inference has been one of the well-known problems in the area of text mining. It deals with the automatic categorisation of words or documents into similarity groups also known as topics. In most of the social media platforms such as Twitter, Instagram, and Facebook, hashtags are used to define the content of posts. Therefore, modelling of hashtags helps in categorising posts as well as analysing user preferences. In this work, we tried to address this problem involving hashtags that stream in real-time. Our approach encompasses graph of hashtags, dynamic sampling and modularity based community detection over the data from a popular social media engagement application. Further, we analysed the topic clusters' structure and quality using empirical experiments. The results unveil latent semantic relations between hashtags and also show frequent hashtags in a cluster. Moreover, in this approach, the words in different languages are treated synonymously. Besides, we also observed top trending topics and correlated clusters.
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ItemCombining usage and content in an online music recommendation system for music in the long-tail( 2012)In this paper we propose a hybrid music recommender system, which combines usage and content data. We describe an online evaluation experiment performed in real time on a commercial music web site, specialised in content from the very long tail of music content. We compare it against two stand-alone recommenders, the first system based on usage and the second one based on content data. The results show that the proposed hybrid recommender shows advantages with respect to usage- and content-based systems, namely, higher user absolute acceptance rate, higher user activity rate and higher user loyalty. Copyright is held by the International World Wide Web Conference Committee (IW3C2).
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ItemA study of machine learning methods for detecting user interest during web sessions( 2014)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.
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ItemA platform to support web site adaptation and monitoring of its effects: A case study( 2008)In this paper we describe a platform that enables Web site automation and monitoring. The platform automatically gathers high quality site activity data, both from the server and client sides. Web adapters, such as rec-ommender systems, can be easily plugged into the platform, and take advantage of the up-to-date activity data. The platform also includes a module to support the editor of the site to monitor and assess the effects of automation. We illustrate the features of the platform on a case study, where we show how it can be used to gather information not only to model the behavior of users but also the impact of the personalization mechanism. Copyright © 2008, Association for the Advancement of Artificial Intelligence.
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