Category-Aware Location Embedding for Point-of-Interest Recommendation

Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation. However, previous studies fail to capture crucial information about POIs such as categorical information. In this paper, we propose a novel neural model that generates a POI embedding incorporating sequential and categorical information from POIs. Our model consists of a check-in module and a category module. The check-in module captures the geographical influence of POIs derived from the sequence of users' check-ins, while the category module captures the characteristics of POIs derived from the category information. To validate the efficacy of the model, we experimented with two large-scale LBSN datasets. Our experimental results demonstrate that our approach significantly outperforms state-of-the-art POI recommendation methods.Comment: 4 pages, 1 figure

A context aware recommender system for tourism with ambient intelligence

Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programming (CSP). It is proposed the architecture according to ambient intelligence perspective to allow the best possible tourist place to the end-user. The key development of this RS is representing the model in CSP and optimizing the problem. We implemented our system in Minizinc solver with domain restrictions represented by user preferences. The CSP allowed user preferences to guide the system toward finding the optimal solutions; RESUMO O sistema de recomendação (RS) detém um lugar significativo na área do sector do turismo. O principal fator do planeamento de viagens é selecionar pontos de interesse relevantes (PoI) do domínio do turismo. O sistema de recomendação (SR) deve recolher informações de comportamentos, personalidade, preferências e outras informações contextuais do utilizador. Este trabalho centra-se principalmente na personalidade, preferências do utilizador e na análise de traços fisiológicos do utilizador. O trabalho tem como objetivo melhorar a modelação do perfil do utilizador, expondo a relação entre a personalidade deste e as categorias dos POI, assim como encontrar uma solução com programação por restrições (CSP). Propõe-se a arquitetura de acordo com a perspetiva do ambiente inteligente para conseguir o melhor lugar turístico possível para o utilizador final. A principal contribuição deste SR é representar o modelo como CSP e tratá-lo como problema de otimização. Implementámos o nosso sistema com o solucionador em Minizinc com restrições de domínio representadas pelas preferências dos utilizadores. O CSP permitiu que as preferências dos utilizadores guiassem o sistema para encontrar as soluções ideais

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