scholarly journals Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Hanwen Liu ◽  
Huaizhen Kou ◽  
Chao Yan ◽  
Lianyong Qi
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Large Scale ◽  
Real Life ◽  
Research Field ◽  
Experimental Results ◽  
The Real ◽  
Matching Technique ◽  
Paper Citation ◽  
Citation Graph

Nowadays, scholar recommender systems often recommend academic papers based on users’ personalized retrieval demands. Typically, a recommender system analyzes the keywords typed by a user and then returns his or her preferred papers, in an efficient and economic manner. In practice, one paper often contains partial keywords that a user is interested in. Therefore, the recommender system needs to return the user a set of papers that collectively covers all the queried keywords. However, existing recommender systems only use the exact keyword matching technique for recommendation decisions, while neglecting the correlation relationships among different papers. As a consequence, it may output a set of papers from multiple disciplines that are different from the user’s real research field. In view of this shortcoming, we propose a keyword-driven and popularity-aware paper recommendation approach based on an undirected paper citation graph, named PRkeyword+pop. At last, we conduct large-scale experiments on the real-life Hep-Th dataset to further demonstrate the usefulness and feasibility of PRkeyword+pop. Experimental results prove the advantages of PRkeyword+pop in searching for a set of satisfactory papers compared with other competitive approaches.

scholarly journals Link prediction in paper citation network to construct paper correlation graph

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Hanwen Liu ◽  
Huaizhen Kou ◽  
Chao Yan ◽  
Lianyong Qi
Keyword(s):  
Recommender System ◽  
Real World ◽  
Link Prediction ◽  
Citation Network ◽  
Experimental Results ◽  
The Real ◽  
Correlation Graph ◽  
Paper Citation ◽  
Prediction Approach

Abstract Nowadays, recommender system has become one of the main tools to search for users’ interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user’s need of keywords. Besides, to satisfy the users’ requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors’ information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance.

scholarly journals A Recommender System for Mobility-as-a-Service Plans Selection

2021 ◽  
Vol 13 (15) ◽  
pp. 8245
Author(s):  
Konstantina Arnaoutaki ◽  
Efthimios Bothos ◽  
Babis Magoutas ◽  
Attila Aba ◽  
Domokos Esztergár-Kiss ◽  
...  
Keyword(s):  
Recommender System ◽  
Constraint Satisfaction ◽  
Smart Cities ◽  
Constraint Satisfaction Problem ◽  
Real Life ◽  
End Users ◽  
Cosine Similarity ◽  
Experimental Results ◽  
The Real ◽  
Novel Concept

Transportation and mobility in smart cities are undergoing a grave transformation as new ways of mobility are introduced to facilitate seamless traveling, addressing travelers’ needs in a personalized manner. A novel concept that has been recently introduced is Mobility-as-a-Service (MaaS), where mobility services are bundled in MaaS Plans and offered to end-users through a single digital platform. The present paper introduces a recommender system for MaaS Plans selection that supports travelers to select bundles of mobility services that fit their everyday transportation needs. The recommender filters out unsuitable plans and then ranks the remaining ones on the basis of their similarity to the users’ characteristics, habits and preferences. The recommendation approach is based on Constraint Satisfaction Problem (CSP) formalisms combined with cosine similarity techniques. The proposed method was evaluated in experimental settings and was further embedded in real-life pilot MaaS applications. The experimental results showed that the proposed approach provides lists of MaaS PlanMaaS Plans that users would choose in a real-life MaaS setting, in most of the cases. Moreover, the results of the real-life pilots showed that the majority of the participants chose an actual MaaS Plan from the top three places of the recommendation lists.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

Clustering Approaches for Top-k Recommender Systems

2019 ◽  
Vol 28 (05) ◽  
pp. 1950019 ◽  
Author(s):  
Nicolás Torres ◽  
Marcelo Mendoza
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Large Scale ◽  
Empirical Studies ◽  
Cluster Structure ◽  
Parallel Architecture ◽  
Evaluation Framework ◽  
Training Data ◽  
Cluster Solution ◽  
Novel Approach

Clustering-based recommender systems bound the seek of similar users within small user clusters providing fast recommendations in large-scale datasets. Then groups can naturally be distributed into different data partitions scaling up in the number of users the recommender system can handle. Unfortunately, while the number of users and items included in a cluster solution increases, the performance in terms of precision of a clustering-based recommender system decreases. We present a novel approach that introduces a cluster-based distance function used for neighborhood computation. In our approach, clusters generated from the training data provide the basis for neighborhood selection. Then, to expand the search of relevant users, we use a novel measure that can exploit the global cluster structure to infer cluster-outside user’s distances. Empirical studies on five widely known benchmark datasets show that our proposal is very competitive in terms of precision, recall, and NDCG. However, the strongest point of our method relies on scalability, reaching speedups of 20× in a sequential computing evaluation framework and up to 100× in a parallel architecture. These results show that an efficient implementation of our cluster-based CF method can handle very large datasets providing also good results in terms of precision, avoiding the high computational costs involved in the application of more sophisticated techniques.

scholarly journals Real Life Human Movement Realization in Multimodal Group Communication Using Depth Map Information and Machine Learning

2020 ◽  
Vol 2 (2) ◽  
pp. 93-101
Author(s):  
Dr. Ranganathan G.
Keyword(s):  
Data Gathering ◽  
Real Life ◽  
Depth Map ◽  
Human Movement ◽  
Recognition System ◽  
Research Field ◽  
Movement Detection ◽  
The Real ◽  
Human Movements ◽  
Almost All

The latest advancements in the evolution of depth map information’s has paved way for interesting works like object recognition sign detection and human movement detection etc. The real life human movement detection or their activity identification is very challenging and tiresome. Since the real life activities of the humans could be of much interest in almost all areas, the subject of identifying the human activities has gained significance and has become a most popular research field. Identifying the human movements /activities in the public places like airport, railways stations, hospital, home for aged become very essential due to the several benefits incurred form the human movement recognition system such as surveillance camera, monitoring devices etc. since the changes in the space and the time parameters can provide an effective way of presenting the movements, yet in the case of natural color vision, as the flatness is depicted in almost all portions of images. So the work laid out in the paper in order to identify the human movement in the real life employs the space and the time depth particulars (Spatial-Temporal depth details –STDD) and the random forest in the final stage for movement classification. The technology put forth utilize the Kinect sensors to collecting the information’s in the data gathering stage. The mechanism laid out to identify the human movements is test with the MATLAB using the Berkley and the Cornell datasets. The mechanism proposed through the acquired results proves to deliver a better performance compared to the human movements captured using the normal video frames.

scholarly journals Session-Based Recommender System for Sustainable Digital Marketing

2019 ◽  
Vol 11 (12) ◽  
pp. 3336 ◽  
Author(s):  
Hyunwoo Hwangbo ◽  
Yangsok Kim
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
High Performance ◽  
Marketing Strategies ◽  
Feature Weighting ◽  
Experimental Results ◽  
Digital Marketing ◽  
Chi Square ◽  
Fashion Products

Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

THE EVOLUTION OF SPIN-OFF VENTURES: AN INTEGRATED MODEL

2010 ◽  
Vol 07 (01) ◽  
pp. 53-70 ◽  
Author(s):  
SVEN H. DE CLEYN ◽  
JOHAN BRAET
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Real Life ◽  
Integrated Model ◽  
Research Field ◽  
Integrative Model ◽  
Life Situation ◽  
Dynamic Nature ◽  
The Real ◽  
Real Life Situation

The article aims to give an overview of the main models in the spin-off research field. The main evolution models known in literature will be analyzed. The evolution models will be discussed in increasing order of complexity. However, the existing models will prove to be inadequate to reflect the real-life situation. Therefore, a new integrative model will be discussed in detail, illustrated by using 17 case studies of Belgian academic spin-offs. The model incorporates the dynamic nature of academic spin-off evolution and the major peripheral aspects. It can be used by practitioners and academics to enhance reproducibility and decision making.

scholarly journals Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System

2021 ◽  
Vol 11 (4) ◽  
pp. 1733
Author(s):  
Yuseok Ban ◽  
Kyungjae Lee
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Experimental Results ◽  
Implicit Feedback ◽  
Digital Platforms ◽  
Novel Approach ◽  
Domain Transfer ◽  
Explicit Feedback

Many studies have been conducted on recommender systems in both the academic and industrial fields, as they are currently broadly used in various digital platforms to make personalized suggestions. Despite the improvement in the accuracy of recommenders, the diversity of interest areas recommended to a user tends to be reduced, and the sparsity of explicit feedback from users has been an important issue for making progress in recommender systems. In this paper, we introduce a novel approach, namely re-enrichment learning, which effectively leverages the implicit logged feedback from users to enhance user retention in a platform by enriching their interest areas. The approach consists of (i) graph-based domain transfer and (ii) metadata saliency, which (i) find an adaptive and collaborative domain representing the relations among many users’ metadata and (ii) extract attentional features from a user’s implicit logged feedback, respectively. The experimental results show that our proposed approach has a better capacity to enrich the diversity of interests of a user by means of implicit feedback and to help recommender systems achieve more balanced personalization. Our approach, finally, helps recommenders improve user retention, i.e., encouraging users to click more items or dwell longer on the platform.

scholarly journals Solid Waste Incineration Modelling for Advanced Moving Grate Incinerators

2020 ◽  
Vol 12 (19) ◽  
pp. 8007
Author(s):  
Mingtao Jiang ◽  
Adrian C. H. Lai ◽  
Adrian Wing-Keung Law
Keyword(s):  
Solid Waste ◽  
Large Scale ◽  
Real Life ◽  
Field Performance ◽  
Field Measurements ◽  
Waste Incineration ◽  
Waste To Energy ◽  
Pyrolysis Kinetics ◽  
The Real ◽  
Actual Field

Currently, the design of advanced moving grate (AMG) incinerators for solid waste is aided by computational simulations. The simulation approach couples a waste bed model to characterize the incineration processes of the waste material on top of the moving grate, with a computational fluid dynamics (CFD) model to reproduce the heated air movement and reactions in the incinerator space above. However, the simulation results of AMG incinerators are rarely compared with actual field measurements for validation in the literature so far. In this study, we first examine the sensitivity of pyrolysis kinetics in the waste bed model using three existing alternatives. The predictions of combustion characteristics, including the bed height, flow and temperature distributions, composition of stack gases and gas emissions are obtained for the three alternatives and compared with measurements from a simple laboratory furnace. The results show that the pyrolysis kinetics mechanism can significantly affect the outputs from the waste bed model for incineration modelling. Subsequently, we propose a new coupling approach based on a recent AMG waste bed model (which includes the complex pyrolysis kinetics inside the waste bed on top of the moving grate) and the freeboard CFD simulations. The new approach is then used to predict the field performance of a large scale waste-to-energy (WTE) plant and the predictions are compared directly with the real measurements in various operational scenarios. The comparison shows an overall satisfactory agreement in terms of temperature and exit gases composition given the complexity of the real life operations, although the CO emission is slightly underpredicted.

scholarly journals Management of e-waste

2020 ◽  
Vol 4 (6) ◽  
pp. 92-98
Keyword(s):  
Waste Management ◽  
End Of Life ◽  
Short Duration ◽  
Large Scale ◽  
Real Life ◽  
Manufacturing Industries ◽  
Human Beings ◽  
The Real ◽  
Market Values ◽  
Shed Light

The sky rocketing growth in various industries were being witnessed due to the rapid advancement in technologies and innovation in a very short duration of time. A large percentage of the population had the capacity to change or replace appliances and gadgets with the new ones launching into the society. This resulted in discarding of the former equipments before reaching it’s end of life duration. Since industries designed and manufactured goods on a large scale, as a result, a lot of manufacturing industries especially those that manufactured electrical and electronics were expelling a lot of waste to the environment. These were the unsold goods and whose market values had dropped due to the newer products taking their place. This not only harmed the disposal grounds but also posed a serious risk to its components like flora, fauna and human beings as well. The paper discussed in brief, about the various steps and procedures that were undertaken to tackle the problem of e-waste management. Countries like Australia had policies implemented to be followed for the sake of waste management. Lastly, the real-life examples of few countries reflecting on how they shed light on issues when it comes to managing their respective wastes along with future predictions and estimations of e-waste.

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