203 research outputs found
Towards MANET-based Recommender Systems for Open Facilities
Nowadays, most recommender systems are based on a centralized architecture, which can cause crucial issues in terms of trust, privacy, dependability, and costs. In this paper, we propose a decentralized and distributed MANET-based (Mobile Ad-hoc NETwork) recommender system for open facilities. The system is based on mobile devices that collect sensor data about users locations to derive implicit ratings that are used for collaborative filtering recommendations. The mechanisms of deriving ratings and propagating them in a MANET network are discussed in detail. Finally, extensive experiments demonstrate the suitability of the approach in terms of different performance metrics. © 2021, The Author(s)
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; âsensingâ, âanalysisâ, and âapplicationâ. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches
In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work
Neuro-Symbolic Recommendation Model based on Logic Query
A recommendation system assists users in finding items that are relevant to
them. Existing recommendation models are primarily based on predicting
relationships between users and items and use complex matching models or
incorporate extensive external information to capture association patterns in
data. However, recommendation is not only a problem of inductive statistics
using data; it is also a cognitive task of reasoning decisions based on
knowledge extracted from information. Hence, a logic system could naturally be
incorporated for the reasoning in a recommendation task. However, although
hard-rule approaches based on logic systems can provide powerful reasoning
ability, they struggle to cope with inconsistent and incomplete knowledge in
real-world tasks, especially for complex tasks such as recommendation.
Therefore, in this paper, we propose a neuro-symbolic recommendation model,
which transforms the user history interactions into a logic expression and then
transforms the recommendation prediction into a query task based on this logic
expression. The logic expressions are then computed based on the modular logic
operations of the neural network. We also construct an implicit logic encoder
to reasonably reduce the complexity of the logic computation. Finally, a user's
interest items can be queried in the vector space based on the computation
results. Experiments on three well-known datasets verified that our method
performs better compared to state of the art shallow, deep, session, and
reasoning models.Comment: 17 pages, 6 figure
Artificial Intelligence for Multimedia Signal Processing
Artificial intelligence technologies are also actively applied to broadcasting and multimedia processing technologies. A lot of research has been conducted in a wide variety of fields, such as content creation, transmission, and security, and these attempts have been made in the past two to three years to improve image, video, speech, and other data compression efficiency in areas related to MPEG media processing technology. Additionally, technologies such as media creation, processing, editing, and creating scenarios are very important areas of research in multimedia processing and engineering. This book contains a collection of some topics broadly across advanced computational intelligence algorithms and technologies for emerging multimedia signal processing as: Computer vision field, speech/sound/text processing, and content analysis/information mining
Recommended from our members
Crowdsourced Data Mining for Urban Activity: A Review of Data Sources, Applications and Methods
The penetration of devices integrated with location-based services and internet services has generated massive data about the everyday life of citizens and tracked their activities happening in cities. Crowdsourced data, such as social media data, POIs data and collaborative websites, generated by the crowd, has become fine-grained proxy data of urban activity and widely used in research in urban studies. However, due to the heterogeneity of data types of crowdsourced data and the limitation of previous studies mainly focusing on a specific application, a systematic review of crowdsourced data mining for urban activity is still lacking. In order to fill the gap, this paper conducts a literature search in the Web of Science database, selecting 226 highly related papers published between 2013 and 2019. Based on those papers, the review firstly conducts a bibliometric analysis identifying underpinning domains, pivot scholars and papers around this topic. The review also synthesises previous research into three parts: main applications of different data sources and data fusion; application of spatial analysis in mobility patterns, functional areas and event detection; application of socio-demographic and perception analysis in city attractiveness, demographic characteristics and sentiment analysis. The challenges of this type of data are also discussed in the end. This study provides a systematic and current review for both researchers and practitioners interested in the applications of crowdsourced data mining for urban activity.This research is funded by a scholarship from the China Scholarship Counci
Review of Point of Interest Recommendation Systems in Location-Based Social Networks
Point of interest recommendation is recently one of the hotspots in the field of location-based social networks and recommendation systems. Understanding the research status of the point of interest recommendation in location-based social networks can provide a direction for the next step of work. The recent literatures of the point of interest recommendation systems are analyzed. Firstly, the definition is introduced, and the difference from traditional recommendation is discussed from three aspects: influencing factors, recommendation approaches and existing problems. Secondly, the general framework of the point of interest recommendation is proposed, which includes data sources, recommendation approaches and evaluation. Based on this framework, the various influencing factors are introduced, the current recommendation algorithms are generalized, and the evaluation metrics are summarized. Meanwhile, the representative works are analyzed, the research contents and characteristics of each type of methods are summarized in detail, and their advantages and limitations are evaluated. Finally, the challenges and potential directions for possible extensions in this filed are summarized and prospected, and the future research trends and development directions are concluded
Text-based Sentiment Analysis and Music Emotion Recognition
Nowadays, with the expansion of social media, large amounts of user-generated
texts like tweets, blog posts or product reviews are shared online. Sentiment polarity
analysis of such texts has become highly attractive and is utilized in recommender
systems, market predictions, business intelligence and more. We also witness deep
learning techniques becoming top performers on those types of tasks. There are
however several problems that need to be solved for efficient use of deep neural
networks on text mining and text polarity analysis.
First of all, deep neural networks are data hungry. They need to be fed with
datasets that are big in size, cleaned and preprocessed as well as properly labeled.
Second, the modern natural language processing concept of word embeddings as a
dense and distributed text feature representation solves sparsity and dimensionality
problems of the traditional bag-of-words model. Still, there are various uncertainties
regarding the use of word vectors: should they be generated from the same dataset
that is used to train the model or it is better to source them from big and popular
collections that work as generic text feature representations? Third, it is not easy for
practitioners to find a simple and highly effective deep learning setup for various
document lengths and types. Recurrent neural networks are weak with longer texts
and optimal convolution-pooling combinations are not easily conceived. It is thus
convenient to have generic neural network architectures that are effective and can
adapt to various texts, encapsulating much of design complexity.
This thesis addresses the above problems to provide methodological and practical
insights for utilizing neural networks on sentiment analysis of texts and achieving
state of the art results. Regarding the first problem, the effectiveness of various
crowdsourcing alternatives is explored and two medium-sized and emotion-labeled
song datasets are created utilizing social tags. One of the research interests of Telecom
Italia was the exploration of relations between music emotional stimulation and
driving style. Consequently, a context-aware music recommender system that aims
to enhance driving comfort and safety was also designed. To address the second
problem, a series of experiments with large text collections of various contents and
domains were conducted. Word embeddings of different parameters were exercised
and results revealed that their quality is influenced (mostly but not only) by the
size of texts they were created from. When working with small text datasets, it is
thus important to source word features from popular and generic word embedding
collections. Regarding the third problem, a series of experiments involving convolutional
and max-pooling neural layers were conducted. Various patterns relating
text properties and network parameters with optimal classification accuracy were
observed. Combining convolutions of words, bigrams, and trigrams with regional
max-pooling layers in a couple of stacks produced the best results. The derived
architecture achieves competitive performance on sentiment polarity analysis of
movie, business and product reviews.
Given that labeled data are becoming the bottleneck of the current deep learning
systems, a future research direction could be the exploration of various data programming
possibilities for constructing even bigger labeled datasets. Investigation
of feature-level or decision-level ensemble techniques in the context of deep neural
networks could also be fruitful. Different feature types do usually represent complementary
characteristics of data. Combining word embedding and traditional text
features or utilizing recurrent networks on document splits and then aggregating the
predictions could further increase prediction accuracy of such models
- âŠ