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Extracting multilayered Communities of Interest from semantic user profiles: Application to group modeling and hybrid recommendations

Iván Cantador, Pablo Castells
2011 Computers in Human Behavior  
Specifically, we outline here how they can be used to model group profiles and make semantic content-based collaborative recommendations.  ...  This paper describes a proposal to automatically identify Communities of Interest from the tastes and preferences expressed by users in personal ontology-based profiles.  ...  Acknowledgements This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02) and the Ministry of Industry, Tourism and Commerce (CENIT-2007(CENIT- -1012.  ... 
doi:10.1016/j.chb.2010.07.027 fatcat:lqy4ojydwreezhkcl6zkeyryce

A multilayer ontology-based hybrid recommendation model

Iván Cantador, Alejandro Bellogín, Pablo Castells
2008 AI Communications  
Such layers correspond to implicit Communities of Interest (CoI), and enable collaborative recommendations of enhanced precision.  ...  We propose a novel hybrid recommendation model in which user preferences and item features are described in terms of semantic concepts defined in domain ontologies.  ...  Acknowledgements This research was supported by the European Commission (FP6-027685 -MESH) and the Spanish Ministry of Science and Education (TIN2005-06885 -S5T).  ... 
doi:10.3233/aic-2008-0437 fatcat:xqz7zrx4jfhexkorvslhxwffuu

An Enhanced Semantic Layer for Hybrid Recommender Systems

Iván Cantador, Pablo Castells, Alejandro Bellogín
2011 International Journal on Semantic Web and Information Systems (IJSWIS)  
To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations.  ...  Challenging issues in their research agenda include the sparsity of user preference data, and the lack of flexibility to incorporate contextual factors in the recommendation methods.  ...  We propose to exploit the links between users and concepts to extract relations among users and derive semantic Communities of Interest (CoI) according to common preferences.  ... 
doi:10.4018/jswis.2011010103 fatcat:q7nptioe6zawzi7de7dj3dezcm

An Application-oriented Review of Deep Learning in Recommender Systems

Jyoti Shokeen, Chhavi Rana
2019 International Journal of Intelligent Systems and Applications  
The development in technology has gifted huge set of alternatives. In the modern era, it is difficult to select relevant items and information from the large amount of available data.  ...  This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications.  ...  Typically, a RS compares the user profile with profile of similar users or it use the past history or behavior of users to recommend items [2] .  ... 
doi:10.5815/ijisa.2019.05.06 fatcat:67fgexfbfjh2no5b3phvohbole

Towards a Semantic Graph-based Recommender System. A Case Study of Cultural Heritage

Sara Qassimi, El Hassan Abdelwahed
2021 Journal of universal computer science (Online)  
place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags.  ...  In this paper, we aim to enhance the cultural heritage visits by suggesting semantically related places that are most likely to interest a visitor.  ...  The model of the Hybrid-RS is trained using the Singular Value Decomposition (SVD) model to extract features and correlation from the user-item matrix.  ... 
doi:10.3897/jucs.70330 fatcat:7m7h5whp4jhvzatit5hbd6hdu4

A SURVEY ON COMPREHENSIVE TRENDS IN RECOMMENDATION SYSTEMS & APPLICATIONS

Ssvr Kumar Addagarla
2019 International Journal of Electronic Commerce Studies  
This paper wellelaborated for the past, present and future scope of the Recommendation System which would be useful for researchers to get familiarity with this domain.  ...  Recommendation System (RS) gains considerable popularity from the past decade in E-Commerce and other allied fields.  ...  This survey found useful in the CF algorithm such as MDP (Markov decision process) for prediction problem and latent semantic index (LSI) is a statistical method to uncover user communities and model interest  ... 
doi:10.7903/ijecs.1705 fatcat:puqvc6uhd5dhppuatartqqq6ki

Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

Luz Santamaria-Granados, Juan Francisco Mendoza-Moreno, Gustavo Ramirez-Gonzalez
2020 Future Internet  
The review highlights the collection, processing, and feature extraction of data from sensors and wearables to detect emotions.  ...  Recommendation systems have overcome the overload of irrelevant information by considering users' preferences and emotional states in the fields of tourism, health, e-commerce, and entertainment.  ...  The grouping of users based on the features extracted from the datasets of social networks has made it possible to detect the relationships between user interests, affective states, and the similarity  ... 
doi:10.3390/fi13010002 fatcat:h6hfzyfe5bdw3hvlqinjmrunmq

Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

Guang Xing Lye, Wai Khuen Cheng, Teik Boon Tan, Chen Wei Hung, Yen-Lin Chen
2020 Sensors  
Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult.  ...  The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20072098 pmid:32276431 pmcid:PMC7181154 fatcat:nflvgee54ze6rox2bbxlogiryi

A survey of job recommender systems

Shaha T. Al-Otaibi
2012 International Journal of Physical Sciences  
The recommender system technology aims to help users in finding items that match their personnel interests; it has a successful usage in e-commerce applications to deal with problems related to information  ...  While such platforms decrease the recruitment time and advertisement cost, they suffer from an inappropriateness of traditional information retrieval techniques like the Boolean search methods.  ...  Memory-based CF methods This makes use of a sample of user-item database to produce prediction. Each user is part of a group of users with similar interests.  ... 
doi:10.5897/ijps12.482 fatcat:rg4pm4zzgrhhbogdxsfswvglam

Deep learning based semantic personalized recommendation system

Sunny Sharma, Vijay Rana, Vivek Kumar
2021 International Journal of Information Management Data Insights  
It aims to assist users to retrieve relevant items from a large repository of contents by providing items or services of likely interest based on examined evidence of the users' preferences and desires  ...  The proposed system recommends personalized sets of videos to users depending on their previous activity on the site and exploits a domain ontology and user items content to the domain concepts.  ...  recommendation group of a user or not.  ... 
doi:10.1016/j.jjimei.2021.100028 fatcat:ckmdqfw725a43icioo4psfqmda

Fashion Recommendation Systems, Models and Methods: A Review

Samit Chakraborty, Md. Saiful Hoque, Naimur Rahman Jeem, Manik Chandra Biswas, Deepayan Bardhan, Edgar Lobaton
2021 Informatics  
On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users.  ...  This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/informatics8030049 fatcat:2djpad5hwraqnb6v24pg4b4a5m

Health App Recommendation System using Ensemble Multimodel Deep Learning

Deepak Chowdary Edara, Department of Computer Science & Engineering, VFSTR Deemed to be University, Vadlamudi, 522213, Guntur, Andhra Pradesh, India., Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
2020 Journal of Engineering Science and Technology Review  
Recommendation Systems perform an extensive survey on the collection of user reviews, preferences and opinions to discover recommendations of suitable applications to the users' community.  ...  related opinions using Latent Semantic Analysis of such aspects in the reviews, and perform the opinion mining from all of the aspects to generate enhanced recommendations with Ensemble Multimodel Deep  ...  To employ the weights of applications with the user profile, we apply our weights to applications like Fig. 5 . 5 is one of the most popular statistical classification models in the pool of supervised  ... 
doi:10.25103/jestr.135.03 fatcat:fkxg4bijk5gpfmjq6dvo77cbte

Extending the Applicability of Recommender Systems: A Multilayer Framework for Matching Human Resources

Tobias Keim
2007 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07)  
Recommender Systems (RS) so far have been applied to many fields of e-commerce in order to assist users in finding the products that best meet their preferences.  ...  In order to address this new field of application for RS, we integrate own prior research into a unified multilayer framework supporting the matching of individuals for recruitment and team staffing processes  ...  The probabilistic hybrid recommendation model is adapted from the probabilistic latent semantic analysis (PLSA) as described in [20] and [21] .  ... 
doi:10.1109/hicss.2007.223 dblp:conf/hicss/Keim07 fatcat:fnk2tn5ebvdz7ci3ipwmmjoc4y

Artificial intelligence in recommender systems

Qian Zhang, Jie Lu, Yaochu Jin
2020 Complex & Intelligent Systems  
This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of  ...  It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer  ...  They aim to develop methods and build models with hybrids of different types of deep neural networks to comprehensively model the user preferences.  ... 
doi:10.1007/s40747-020-00212-w fatcat:ev3cyoy2mjeuhmq3rymkx2shsy

Recommendation system based on heterogeneous feature: A survey

Hui Wang, ZiChun Le, Xuan Gong
2020 IEEE Access  
GROUP RECOMMENDATION The application of recommendation systems is becoming more extensive, and the object of recommendation has evolved from single-user and personalized recommendations to group recommendation  ...  [49] used WordNet to build a user profile based on semantics using machine learning and text classification algorithms, which contained user preference semantic information, not just keywords.  ... 
doi:10.1109/access.2020.3024154 fatcat:clxk77bcr5hdjd3hnxxi6wzlr4
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