Mohammad Aliannejadi

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This technical report presents the work of the University of Lugano at TREC 2015 Contextual Suggestion and Temporal Summarization tracks. The first track that we report on, is the Contextual Suggestion. The goal of the Contextual Suggestion track is to develop systems that could generate user-specific suggestions that a user might potentially like. Our(More)
Personalized venue suggestion plays a crucial role in satisfying the users needs on location-based social networks (LBSNs). In this study, we present a probabilistic generative model to map user tags to venue taste keywords. We study four approaches to address the data sparsity problem with the aid of such mapping: one model to boost venue taste keywords(More)
Making personalized and context-aware suggestions of venues to the users is very crucial in venue recommendation. These suggestions are often based on matching venues' features with users' preferences, which can be collected from previously visited locations. In this paper we present a novel user-modeling approach which relies on a set of scoring functions(More)
Œis technical report presents the work of Università della Svizzera italiana (USI) at TREC 2016 Contextual Suggestion track. Œe goal of the Contextual Suggestion track is to develop systems that could make suggestions for venues that a user will potentially like. Our proposed method aŠempts to model the users’ behavior and opinion by training a SVM(More)
Suggesting personalized venues helps users to find interesting places on location-based social networks (LBSNs). Although there are many LBSNs online, none of them is known to have thorough information about all venues. The Contextual Suggestion track at TREC aimed at providing a collection consisting of places as well as user context to enable researchers(More)
Personalized context-aware venue suggestion plays a critical role in satisfying the users' needs on location-based social networks (LBSNs). In this paper, we present a set of novel scores to measure the similarity between a user and a candidate venue in a new city. The scores are based on user's history of preferences in other cities as well as user's(More)
We experiment graph-based SemiSupervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language Understanding (SLU) on unaligned data. The aligned labels for examples are obtained using IBM Model. We adapt a baseline semisupervised CRF by defining new feature set and altering the label propagation algorithm. Our results(More)
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