Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation

Venue recommendation aims to assist users by making personalised suggestions of venues to visit, building upon data available from location-based social networks (LBSNs) such as Foursquare. A particular challenge for this task is context-aware venue recommendation (CAVR), which additionally takes the surrounding context of the user (e.g. the user’s location and the time of day) into account in order to provide more relevant venue suggestions. To address the challenges of CAVR, we describe two approaches that exploit word embedding techniques to infer the vector-space representations of venues, users’ existing preferences, and users’ contextual preferences. Our evaluation upon the test collection of the TREC 2015 Contextual Suggestion track demonstrates that we can significantly enhance the effectiveness of a state-of-the-art venue recommendation approach, as well as produce context-aware recommendations that are at least as effective as the top TREC 2015 systems

Recommendation of Tourist Points of Interest using the Web as source

Este artículo presenta un sistema de recomendación híbrido, basado en contenido y comunidad de usuarios, para recomendar a los usuarios los lugares próximos más afines a sus gustos. El contenido se extrae de forma automática de la web oficial del punto de interés. Destacamos los buenos resultados obtenidos cuando la información recuperada para cada lugar de su sitio web es descriptiva. Nuestros experimentos se han realizado sobre los datos ofrecidos por la organización del Contextual Suggestion Track en TREC 2014, una tarea exigente donde la información de los usuarios es dispersa y cuyas recomendaciones se deben obtener a partir de coordenadas geográficas y poca información adicional.This paper introduces a hybrid recommender system, based on both content and community of users, to suggest places according to user's interests. The content has been automatically extracted from official web page of each place. We remark the promising results obtained when the official web site provides descriptive content. Our experiments have been performed on the Contextual Suggestion Track dataset from TREC 2014, a competitive task where information about users is very sparse and recommendations must come from only GPS coordinates and few additional information.Este trabajo se ha desarrollado gracias a la financiación parcial del proyecto ATTOS (TIN2012-38536-C03-0) del Gobierno de España y del proyecto CEATIC-2013-01 de la Universidad de Jaén

Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation

Venue recommendation aims to assist users by making personalised suggestions of venues to visit, building upon data available from location-based social networks (LBSNs) such as Foursquare. A particular challenge for this task is context-aware venue recommendation (CAVR), which additionally takes the surrounding context of the user (e.g. the user’s location and the time of day) into account in order to provide more relevant venue suggestions. To address the challenges of CAVR, we describe two approaches that exploit word embedding techniques to infer the vector-space representations of venues, users’ existing preferences, and users’ contextual preferences. Our evaluation upon the test collection of the TREC 2015 Contextual Suggestion track demonstrates that we can significantly enhance the effectiveness of a state-of-the-art venue recommendation approach, as well as produce context-aware recommendations that are at least as effective as the top TREC 2015 systems

Suggestion contextuelle composite

International audienceLa suggestion contextuelle consiste à recommander à un utilisateur un ensemble de lieux d'activités adaptés à ses préférences et à son contexte. La plupart des approches existantes considèrent uniquement ces deux caractéristiques pour constituer leur liste de suggestions. Cependant, les recherches en systèmes de recommandation ont récemment souligné l'importance de la diversité des suggestions. Cet article présente un modèle novateur de suggestion contextuelle inspiré de la recherche composite qui consiste à regrouper les suggestions en différentes grappes thématiquement cohésives. L'évaluation réalisée dans le cadre de la piste Contextual Suggestion de TREC 2013 et 2014 montre que notre approche est compétitive et permet d'améliorer la diversité des suggestions sans dégrader leur pertinence

Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19

This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the 2020 TREC-COVID Challenge) organised by the U.S. National Institute of Standards and Technology (NIST) and its Text Retrieval Conference (TREC) division. The aim of the competition was to find the best search engine strategy for retrieving precise biomedical scientific information on COVID-19 from the largest, at that point in time, dataset of curated scientific literature on COVID-19 -- the COVID-19 Open Research Dataset (CORD-19). CORD-19 was the result of a call to action to the tech community by the U.S. White House in March 2020, and was shortly thereafter posted on Kaggle as an AI competition by the Allen Institute for AI, the Chan Zuckerberg Initiative, Georgetown University's Center for Security and Emerging Technology, Microsoft, and the National Library of Medicine at the US National Institutes of Health. CORD-19 contained over 200,000 scholarly articles (of which more than 100,000 were with full text) about COVID-19, SARS-CoV-2, and related coronaviruses, gathered from curated biomedical sources. The TREC-COVID challenge asked for the best way to (a) retrieve accurate and precise scientific information, in response to some queries formulated by biomedical experts, and (b) rank this information decreasingly by its relevance to the query. In this document, we describe the TREC-COVID competition setup, our participation to it, and our resulting reflections and lessons learned about the state-of-art technology when faced with the acute task of retrieving precise scientific information from a rapidly growing corpus of literature, in response to highly specialised queries, in the middle of a pandemic

An Evaluation of Contextual Suggestion

This thesis examines techniques that can be used to evaluate systems that solve the complex task of suggesting points of interest to users. A traveller visiting an unfamiliar, foreign city might be looking for a place to have fun in the last few hours before returning home. Our traveller might browse various search engines and travel websites to find something that he is interested in doing, however this process is time consuming and the visitor may want to find some suggestion quickly. We will consider the type of system that is able to handle this complex request in such a way that the user is satisfied. Because the type of suggestion one person wants will differ from the type of suggestion another person wants we will consider systems that incorporate some level of personalization. In this work we will develop user profiles that are based on real users and set up experiments that many research groups can participate in, competing to develop the best techniques for implementing this kind of system. These systems will make suggestion of attractions to visit in various different US cities to many users. This thesis is divided into two stages. During the first stage we will look at what information will go into our user profiles and what information we need to know about the users in order to decide whether they would visit an attraction. The second stage will be deciding how to evaluate the suggestions that various systems make in order to determine which system is able to make the best suggestions

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

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