17 research outputs found

    A Comprehensive Survey on Comparisons across Contextual Pre-filtering, Contextual Post-filtering and Contextual Modelling Approaches

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    Recently, there has been growing interest in recommender systems (RS) and particularly in context-aware RS. Methods for generating context-aware recommendations are classified into pre-filtering, post-filtering and contextual modelling approaches. In this paper, we present the several novel approaches of the different variant of each of these three contextualization paradigms and present a complete survey on the state-of-the-art comparisons across them. We then identify the significant challenges that require being addressed by the current RS researchers, which will help academicians and practitioners in comparing these three approaches to select the best alternative according to their strategies

    A review of the role of sensors in mobile context-aware recommendation systems

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    Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

    Deep interest shifting network with meta embeddings for fresh item recommendation

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    Nowadays, people have an increasing interest in fresh products such as new shoes and cosmetics. To this end, an E-commerce platform Taobao launched a fresh-item hub page on the recommender system, with which customers can freely and exclusively explore and purchase fresh items, namely, the New Tendency page. In this work, we make a first attempt to tackle the fresh-item recommendation task with two major challenges. First, a fresh-item recommendation scenario usually faces the challenge that the training data are highly deficient due to low page views. In this paper, we propose a deep interest-shifting network (DisNet), which transfers knowledge from a huge number of auxiliary data and then shifts user interests with contextual information. Furthermore, three interpretable interest-shifting operators are introduced. Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem. Though this problem can be alleviated by knowledge transfer, we further babysit these fully cold-start items by a relational meta-Id-embedding generator (RM-IdEG). Specifically, it trains the item id embeddings in a learning-to-learn manner and integrates relational information for better embedding performance. We conducted comprehensive experiments on both synthetic datasets as well as a real-world dataset. Both DisNet and RM-IdEG significantly outperform state-of-the-art approaches, respectively. Empirical results clearly verify the effectiveness of the proposed techniques, which are arguably promising and scalable in real-world applications

    Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

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    Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design

    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 /

    Context-Based Cultural Visits

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    Over the last two decades, there have been tremendous advances in mobile technologies, which have increased the interest in studying and developing mobile augmented reality systems, especially in the field of Cultural Heritage. Nowadays, people rely even more on smartphones, for example, when visiting a new city to search for information about monuments and landmarks, and the visitor expects precise and tailored information to his needs. Therefore, researchers started to investigate innovative approaches for presenting and suggesting digital content related to cultural and historical places around the city, incorporating contextual information about the visitor and his needs. This document presents a novel mobile augmented reality application, NearHeritage, that was developed within the scope of the master's thesis on Electrical and Computers Engineering from the Faculty of Engineering of Porto University (FEUP), in collaboration with INESC TEC. The research carried out was focused on the importance of utilising modern technologies to assist the visitors in finding and exploring Cultural Heritage. In this way, it is provided not only the nearby points-of-interest of a city but also detailed information about each POI. The solution presented uses built-in sensors and hardware of Android devices and takes advantage of various APIs (Foursquare API, Google Maps API and IntelContextSensing) to retrieve information about the landmarks and the visitor context. Also, these are crucial hardware components for implementing the full potential of augmented reality tools to create innovative contents that increase the overall user experience. All the experiments were conducted in Porto, Portugal, and the final results showcase that the concept of a MAR application can improve the user experience in discovering and learning more about Cultural Heritage around the world, creating an interactive, enjoyable and unforgettable adventure

    An integrated recommender system for improved accuracy and aggregate diversity

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    Information explosion creates dilemma in finding preferred products from the digital marketplaces. Thus, it is challenging for online companies to develop an efficient recommender system for large portfolio of products. The aim of this research is to develop an integrated recommender system model for online companies, with the ability of providing personalized services to their customers. The K-nearest neighbors (KNN) algorithm uses similarity matrices for performing the recommendation system; however, multiple drawbacks associated with the conventional KNN algorithm have been identified. Thus, an algorithm considering weight metric is used to select only significant nearest neighbors (SNN). Using secondary dataset on MovieLens and combining four types of prediction models, the study develops an integrated recommender system model to identify SNN and predict accurate personalized recommendations at lower computation cost. A timestamp used in the integrated model improves the performance of the personalized recommender system. The research contributes to behavioral analytics and recommender system literature by providing an integrated decision-making model for improved accuracy and aggregate diversity. The proposed prediction model helps to improve the profitability of online companies by selling diverse and preferred portfolio of products to their customers

    When linguistics meets web technologies. Recent advances in modelling linguistic linked data

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    This article provides an up-to-date and comprehensive survey of models (including vocabularies, taxonomies and ontologies) used for representing linguistic linked data (LLD). It focuses on the latest developments in the area and both builds upon and complements previous works covering similar territory. The article begins with an overview of recent trends which have had an impact on linked data models and vocabularies, such as the growing influence of the FAIR guidelines, the funding of several major projects in which LLD is a key component, and the increasing importance of the relationship of the digital humanities with LLD. Next, we give an overview of some of the most well known vocabularies and models in LLD. After this we look at some of the latest developments in community standards and initiatives such as OntoLex-Lemon as well as recent work which has been in carried out in corpora and annotation and LLD including a discussion of the LLD metadata vocabularies META-SHARE and lime and language identifiers. In the following part of the paper we look at work which has been realised in a number of recent projects and which has a significant impact on LLD vocabularies and models
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