16,699 research outputs found

    Influence diagrams for contextual information retrieval

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    International audienceThe purpose of contextual information retrieval is to make some exploration towards designing user specific search engines that are able to adapt the retrieval model to the variety of differences on user's contexts. In this paper we propose an influence diagram based retrieval model which is able to incorporate contexts, viewed as user's long-term interests into the retrieval process

    A Contextual Information Retrieval Model based on Influence Diagrams

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    International audienceA key challenge in information retrieval is the use of contex- tual evidence within the ad-hoc retrieval. Our contribution is particularly based on the belief that contextual retrieval is a decision-making prob- lem. For this reason we propose to apply influence diagrams witch are an extension of Bayesian networks to such problems, in order to solve the hard problem of user based relevance estimation. The basic underlying idea is to substitute to the traditional relevance function which measures the degree of matching document-query, a function indexed by the user. In our approach, the user profile is represented by his long term interests. In order to validate our model, we propose furthermore a novel evaluation protocol suitable for the contextual retrieval task. The test collection is an expansion of the standard TREC test data, obtained using a learning scenario of the user's interests. The experimental results show that our model is promising

    EU accession and Poland's external trade policy

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    Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation

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    Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches
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