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Improving Collaborative Recommendation via Location-based User-item Subgroup

Zhi Qiao, Peng Zhang, Yanan Cao, Chuan Zhou, Li Guo
2014 Procedia Computer Science  
It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group.  ...  Recently due to the wide use of mobile devices and ubiquitous sensors, it is urgent to incorporate location-based information in rating an item [1].  ...  The subgroup analysis has been proposed for improving accuracy. While, we find that geographical information of user have impacts on user group preference for items.  ... 
doi:10.1016/j.procs.2014.05.036 fatcat:5u4n2mi7tze2foelarotr5e7xa

Group-Aware Recommendation using Random Forest Classification for Sparsity Problem

D. Agalya, V. Subramaniyaswamy
2016 Indian Journal of Science and Technology  
To overcome this issue, we propose a group recommendation, which divides a larger task into smaller tasks to the subgroups. Many existing approaches considered only isolated subgroups.  ...  Methods: In this work, we proposed top-N recommendation algorithm by considering the interrelationship between each group which leads to an efficient and accurate way to recommend items.  ...  Content-based RS, Collaborative filtering RS and Hybrid based RS are the three different categories of RS. Content-based recommender system, which predicts item based on user profile 1, 2 .  ... 
doi:10.17485/ijst/2016/v9i48/107960 fatcat:mscko5jgnfe43o5higic7je26i

A Novel Recommendation Algorithm Based on Clustering Dissimilarity Measures

Liang Zhang, School of Economics and Management, Guizhou Normal University, Guiyang 550001, China, Xiaojing Liu, Xue Zhou
2020 Journal of Engineering Science and Technology Review  
This study provides references for improving the satisfaction of users with recommendation systems.  ...  To recommend diverse items under the precondition of guaranteeing accuracy, a clustering-based novel recommendation algorithm was proposed in this study.  ...  For instance, Feng, L. et al. divided users and items into several subgroups, trained the traditional collaborative filtering model for each ______________ *E-mail address: ftygygz@vip.sina.com ISSN: 1791  ... 
doi:10.25103/jestr.133.10 fatcat:d6wvnqqm2zajrdbzn2mz7smf6u

Recommendations Based on Social Links [chapter]

Danielle Lee, Peter Brusilovsky
2018 Lecture Notes in Computer Science  
Then, the in-depth understanding of existing recommendations based on users' social links will be addressed.  ...  items, not social links of interests.  ...  [130] has explored how to recommend places of interests via users' check-in locations and their online social networks.  ... 
doi:10.1007/978-3-319-90092-6_11 fatcat:5nhd7rbgu5d3fc563mrhj2nnzi

Multi-Objective Optimization Based Location and Social Network Aware Recommendation

Makbule Gulcin Ozsoy, Faruk Polat, Reda Alhajj
2014 Proceedings of the 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing  
Social networks, personal blog pages, on-line transaction web-sites, expertise web pages and location based social networks provide an attractive platform for millions of users to share opinions, comments  ...  Also, taking into account more than one criterion can improve the performance of the recommender systems.  ...  While the content based approach uses item similarity to give recommendations, collaborative filtering uses past preferences of the users to decide which item to recommend.  ... 
doi:10.4108/icst.collaboratecom.2014.257382 dblp:conf/colcom/OzsoyPA14 fatcat:7h3fgffewnexhpqjrhxt74ar6y

Personalised community maps

Liliana Ardissono, Maurizio Lucenteforte, Noemi Mauro, Adriano Savoca, Angioletta Voghera
2017 International Journal of Electronic Governance  
Our model builds on tag-based user profiles and on information filtering. This paper describes our model and the results of an evaluation of the GroupMapping application, based on it.  ...  Both cases raise challenges related to the large amount of data handled in the maps, and to their lack of group collaboration support.  ...  Acknowledgements The authors thank the anonymous reviewers for their valuable comments that considerably contributed to improving the final version of the paper.  ... 
doi:10.1504/ijeg.2017.084648 fatcat:xufd3alfwbbefmez4sr6v5kzqe

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Nor Aniza Abdullah, Rasheed Abubakar Rasheed, Mohd Hairul Nizam Md. Nasir, Md Mujibur Rahman
2021 Applied Sciences  
Recommender systems suggest items of interest to users based on their preferences. These preferences are typically generated from user ratings of the items.  ...  If there are no ratings for a certain user or item, it is said that there is a cold start problem, which leads to unreliable recommendations.  ...  Recommender systems are software tools that recommend users to items or items of interest to users based on their preferences [3] .  ... 
doi:10.3390/app11209608 fatcat:foxbu3gt4fdxhdxuhijtufkyiu

Contribution to Collaborative Filtering Based on Soft Computing to Enhance Recommender System for e-Commerce

Saad Darwish
2014 International Journal of e-Education, e-Business, e-Management and e-Learning  
Recommender Systems (RSs) are used by an ever-increasing number of e-commerce sites to recommend items of interest to the users based on their preferences.  ...  Collaborative filtering is one of the most regularly used techniques in RSs that help the users to catch the items of interest from a massive numbers of available items.  ...  The academics in [7] extend traditional clustering CF models by co-clustering both users and items into multiple subgroups, and use them to improve the performance of CF-based recommender systems.  ... 
doi:10.7763/ijeeee.2014.v4.341 fatcat:pvj4dwhwvbetfi5b5lvcnxymxa

A SURVEY OF CONTEXT-AWARE MOBILE RECOMMENDATIONS

QI LIU, HAIPING MA, ENHONG CHEN, HUI XIONG
2013 International Journal of Information Technology and Decision Making  
In contrast, in collaborativē lterings 102 the given user will be recommended the items that people with similar tastes and preferences liked in the past (also noted as user-based collaborative¯lterings  ...  ¯lterings have been improved dramatically by incorporating some kind of additional sources of content information about the users or items.  ...  Lee et al. 65 proposed a collaborative¯ltering-based recommendation methodology based on both implicit mobile user ratings and less ambitious ordinal scales.  ... 
doi:10.1142/s0219622013500077 fatcat:q7fvqibeonaz3nwah6s5t4pfhy

(Partial) user preference similarity as classification-based model similarity

Amancio Bouza, Abraham Bernstein
2014 Semantic Web Journal  
Recommender systems play an important role in helping people finding items they like. One type of recommender system is collaborative filtering that considers feedback of like-minded people.  ...  Recommender systems play an important role in helping people finding items they like. One type of recommender system is collaborative filtering that considers feedback of like-minded people.  ...  A user can belong to multiple user-item subgroups. To recommend an item to a user, the users within the corresponding subgroups are used to predict the relevance of the particular item.  ... 
doi:10.3233/sw-130099 fatcat:2qyi243ctfb2tk6mo6d3nbn7qe

From product recommendation to cyber-attack prediction: Generating attack graphs and predicting future attacks [article]

Nikolaos Polatidis, Elias Pimenidis, Michalis Pavlidis, Spyridon Papastergiou, Haralambos Mouratidis
2018 arXiv   pre-print
Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks.  ...  The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management.  ...  In addition to the methods mentioned already the use of user-item subgroups has been proposed as a way of providing improved recommendation systems (Xu, Bu, Chen, & Cai, 2012) .  ... 
arXiv:1804.10276v1 fatcat:4ff3mkzjavcarpgz5lsmvo3qke

Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices

Liliana Ardissono, Anna Goy, Giovanna Petrone, Marino Segnan, Pietro Torasso
2003 Applied Artificial Intelligence  
The services offered by INTRIGUE rely on user modeling and adaptive hypermedia techniques; furthermore, XML-based technologies support the generation of the user interface and its adaptation to Web browsers  ...  This system recommends sightseeing destinations and itineraries by taking into account the preferences of heterogeneous tourist groups (such as families with children and elderly) and explains the recommendations  ...  For instance, as described in (Herlocker et al. 2000) , collaborative filtering systems typically explain their own recommendations by reporting the ratings of items provided by people with selection  ... 
doi:10.1080/713827254 fatcat:tpgxyltddzgq5n6u5vpl7y746e

From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

Nikolaos Polatidis, Elias Pimenidis, Michalis Pavlidis, Spyridon Papastergiou, Haralambos Mouratidis
2018 Evolving Systems  
Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks.  ...  The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management.  ...  In addition to the methods mentioned already the use of user-item subgroups has been proposed as a way of providing improved recommendation systems (Xu, Bu, Chen, & Cai, 2012) .  ... 
doi:10.1007/s12530-018-9234-z fatcat:fmr4a7xdnrbs5pbirgv5ee6zru

Recommendations in location-based social networks: a survey

Jie Bao, Yu Zheng, David Wilkie, Mohamed Mokbel
2015 Geoinformatica  
Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies.  ...  First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media.  ...  The traditional CF models can be divided into two subgroups: 1) user-based models, such as [44] , that use similarity measures between each pair of users; and 2) item-based models, such as [53] , that  ... 
doi:10.1007/s10707-014-0220-8 fatcat:3ivmtrnvkfhshl72gd33h4aola

Similarity-Based Clustering and Security Assurance Model for Big Data Processing in Cloud Environment

PARTHIBAN KRISHNAMOORTHY, SUJATHA SUNDARAM
2018 ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH  
A robust form of subset formulation via user-item and item-item similarity promotes the existence of resourceful associative similarity in between that user query and web service.  ...  A prevailing search engine usually opts for providing recommendations for the query being posed by the user through utilization of a prevalent Collaborative Filtering methodology.  ...  Top-N number of recommendations were derived by means of collating subgroups created and the conventional Collaborative Filtering (CF) algorithms.  ... 
doi:10.24818/18423264/52.2.18.11 fatcat:c6etxjr3kzdvbbwlt4pvgbtud4
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