9 research outputs found

    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

    A Factored Relevance Model for Contextual Point-of-Interest Recommendation

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    The challenge of providing personalized and contextually appropriate recommendations to a user is faced in a range of use-cases, e.g., recommendations for movies, places to visit, articles to read etc. In this paper, we focus on one such application, namely that of suggesting 'points of interest' (POIs) to a user given her current location, by leveraging relevant information from her past preferences. An automated contextual recommendation algorithm is likely to work well if it can extract information from the preference history of a user (exploitation) and effectively combine it with information from the user's current context (exploration) to predict an item's 'usefulness' in the new context. To balance this trade-off between exploration and exploitation, we propose a generic unsupervised framework involving a factored relevance model (FRLM), comprising two distinct components, one corresponding to the historical information from past contexts, and the other pertaining to the information from the local context. Our experiments are conducted on the TREC contextual suggestion (TREC-CS) 2016 dataset. The results of our experiments demonstrate the effectiveness of our proposed approach in comparison to a number of standard IR and recommender-based baselines

    Régularisation spatiale de représentations distribuées de mots

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    StimulĂ©e par l’usage intensif des tĂ©lĂ©phones mobiles, l’exploitation conjointe des don-nĂ©es textuelles et des donnĂ©es spatiales prĂ©sentes dans les objets spatio-textuels (p. ex. tweets)est devenue la pierre angulaire Ă  de nombreuses applications comme la recherche de lieux d’attraction. Du point de vue scientifique, ces tĂąches reposent de façon critique sur la reprĂ©sentation d’objets spatiaux et la dĂ©finition de fonctions d’appariement entre ces objets. Dans cet article,nous nous intĂ©ressons au problĂšme de reprĂ©sentation de ces objets. Plus spĂ©cifiquement, confortĂ©s par le succĂšs des reprĂ©sentations distribuĂ©es basĂ©es sur les approches neuronales, nous proposons de rĂ©gulariser les reprĂ©sentations distribuĂ©es de mots (c.-Ă -d. plongements lexicaux ou word embeddings), pouvant ĂȘtre combinĂ©es pour construire des reprĂ©sentations d’objets,grĂące Ă  leurs rĂ©partitions spatiales. L’objectif sous-jacent est de rĂ©vĂ©ler d’éventuelles relations sĂ©mantiques locales entre mots ainsi que la multiplicitĂ© des sens d’un mĂȘme mot. Les expĂ©rimentations basĂ©es sur une tĂąche de recherche d’information qui consiste Ă  retourner le lieu physique faisant l’objet (sujet) d’un gĂ©o-texte montrent que l’intĂ©gration notre mĂ©thode de rĂ©gularisation spatiale de reprĂ©sentations distribuĂ©es de mots dans un modĂšle d’appariement de base permet d’obtenir des amĂ©liorations significatives par rapport aux modĂšles de rĂ©fĂ©rence

    Modeling user information needs on mobile devices: from recommendation to conversation

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    Recent advances in the development of mobile devices, equipped with multiple sensors, together with the availability of millions of applications have made these devices more pervasive in our lives than ever. The availability of the diverse set of sensors, as well as high computational power, enable information retrieval (IR) systems to sense a user’s context and personalize their results accordingly. Relevant studies show that people use their mobile devices to access information in a wide range of topics in various contextual situations, highlighting the fact that modeling user information need on mobile devices involves studying several means of information access. In this thesis, we study three major aspects of information access on mobile devices. First, we focus on proactive approaches to modeling users for venue suggestion. We investigate three methods of user modeling, namely, content-based, collaborative, and hybrid, focusing on personalization and context-awareness. We propose a two-phase collaborative ranking algorithm for leveraging users’ implicit feedback while incorporating temporal and geographical information into the model. We then extend our collaborative model to include multiple cross-venue similarity scores and combine it with our content-based approach to produce a hybrid recommendation. Second, we introduce and investigate a new task on mobile search, that is, unified mobile search. We take the first step in defining, studying, and modeling this task by collecting two datasets and conducting experiments on one of the main components of unified mobile search frameworks, that is target apps selection. To this end, we propose two neural approaches. Finally, we address the conversational aspect of mobile search where we propose an offline evaluation protocol and build a dataset for asking clarifying questions for conversational search. Also, we propose a retrieval framework consisting of three main components: question retrieval, question selection, and document retrieval. The experiments and analyses indicate that asking clarifying questions should be an essential part of a conversational system, resulting in high performance gain

    Efficient query processing for scalable web search

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    Search engines are exceptionally important tools for accessing information in today’s world. In satisfying the information needs of millions of users, the effectiveness (the quality of the search results) and the efficiency (the speed at which the results are returned to the users) of a search engine are two goals that form a natural trade-off, as techniques that improve the effectiveness of the search engine can also make it less efficient. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for the development of efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency. This survey comprehensively reviews the foundations of search engines, from index layouts to basic term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing strategies, while also providing the latest trends in the literature in efficient query processing, including the coherent and systematic reviews of techniques such as dynamic pruning and impact-sorted posting lists as well as their variants and optimisations. Our explanations of query processing strategies, for instance the WAND and BMW dynamic pruning algorithms, are presented with illustrative figures showing how the processing state changes as the algorithms progress. Moreover, acknowledging the recent trends in applying a cascading infrastructure within search systems, this survey describes techniques for efficiently integrating effective learned models, such as those obtained from learning-to-rank techniques. The survey also covers the selective application of query processing techniques, often achieved by predicting the response times of the search engine (known as query efficiency prediction), and making per-query tradeoffs between efficiency and effectiveness to ensure that the required retrieval speed targets can be met. Finally, the survey concludes with a summary of open directions in efficient search infrastructures, namely the use of signatures, real-time, energy-efficient and modern hardware and software architectures

    Context-Aware Recommendation Systems in Mobile Environments

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    Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /
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