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Exploiting web scraping in a collaborative filtering- based approach to web advertising

Eloisa Vargiu, Mirko Urru
2012 Artificial intelligence research  
To this end, we propose a collaborative filtering-based Web advertising system aimed at finding the most relevant ads for a generic Web page by exploiting Web scraping.  ...  In particular, we adopt scraping techniques in the Web advertising field.  ...  The idea to exploit collaborative filtering in a Web advertising has been proposed by Armano & Vargiu [4] and adopted also in Armano et al. [5] .  ... 
doi:10.5430/air.v2n1p44 fatcat:3qa7yuraszfbngclp7l65ttdmq

Learning to Predict Ad Clicks Based on Boosted Collaborative Filtering

Teng-Kai Fan, Chia-Hui Chang
2010 2010 IEEE Second International Conference on Social Computing  
Finally, an effective learning-based framework is proposed to combine filtering models to improve social advertising.  ...  In this study, we first propose the notion of social filtering and compare it with content-based filtering and collaborative filtering for advertisement allocation in a social network.  ...  The work was supported in part by the National Science Council of Taiwan (NSC98-2221-E-008-089).  ... 
doi:10.1109/socialcom.2010.37 dblp:conf/socialcom/FanC10 fatcat:5fqbfbil2bf5tns3walugxonly

We Know What You Want: An Advertising Strategy Recommender System for Online Advertising [article]

Liyi Guo, Junqi Jin, Haoqi Zhang, Zhenzhe Zheng, Zhiye Yang, Zhizhuang Xing, Fei Pan, Lvyin Niu, Fan Wu, Haiyang Xu, Chuan Yu, Yuning Jiang (+1 others)
2021 arXiv   pre-print
We further augment this prototype system by directly revealing the advertising performance, and then infer the advertisers' marketing objectives through their adoptions of different recommending advertising  ...  Simulation experiments based on Taobao online bidding data show that the designed contextual bandit algorithm can effectively optimize the strategy adoption rate of advertisers.  ...  Hybrid filtering [1] combines collaborative filtering and content-based filtering to improve the accuracy of recommendations.  ... 
arXiv:2105.14188v2 fatcat:t7n42qqo7ffifgfiyzipsgel34

Flow Moods: Recommending Music by Moods on Deezer [article]

Théo Bontempelli and Benjamin Chapus and François Rigaud and Mathieu Morlon and Marin Lorant and Guillaume Salha-Galvan
2022 arXiv   pre-print
Flow Moods leverages collaborative filtering, audio content analysis, and mood annotations from professional music curators to generate personalized mood-specific playlists at scale.  ...  In this paper, we present Flow Moods, an improved version of Flow that addresses this limitation.  ...  To generate these playlists, we leverage a latent model for collaborative filtering [3, 16] .  ... 
arXiv:2207.11229v1 fatcat:u2pbwxftdnamnnom2pt2hsotna

Mobile Multimedia Recommendation in Smart Communities: A Survey [article]

Feng Xia, Nana Yaw Asabere, Ahmedin Mohammed Ahmed, Jing Li, Xiangjie Kong
2013 arXiv   pre-print
A cautious analysis of existing research reveals that the implementation of proactive, sensor-based and hybrid recommender systems can improve mobile multimedia recommendations.  ...  Mobile advertising during event (conference, tradeshows, seminars etc.) guides is very important for participants to express themselves further and improve event collaboration.  ...  The approach forms two document prototypes by taking the vector sum over all relevant and non-relevant documents. 5) Contextual Modeling, Pre-Filtering and Post-Filtering Algorithms The contextual modeling  ... 
arXiv:1312.6565v1 fatcat:myep75jg4fcvhgscrxjmouoksm

Personalized and mobile digital TV applications

Konstantinos Chorianopoulos
2007 Multimedia tools and applications  
In this context, personalization research is concerned with the adaptation of content (e.g. movies, news, advertisements).  ...  The above developments raise novel issues and require the adoption of new multimedia standards and application frameworks.  ...  Collaborative filtering is characterized by its independence from item's features, which makes it applicable to almost any type of content.  ... 
doi:10.1007/s11042-006-0081-8 fatcat:ii7qyuyvnfhobdumbm4mujouhe

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Areej Bin Suhaim, Jawad Berri
2021 IEEE Access  
This has led to more research being done in this area stimulated by the omnipresence of smartphones and the latest web technologies.  ...  INDEX TERMS Context-aware system, Contextual factors, Recommender system, Social network Areej Bin-Suhaim received the B.S. degree in information technology from King Saud University (KSU), Riyadh, Saudi  ...  ACKNOWLEDGEMENTS This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University. The authors are grateful for this support.  ... 
doi:10.1109/access.2021.3072165 fatcat:i3igbxd44jhrzcyvynevpidcwq

Epistemic fragmentation poses a threat to the governance of online targeting

Silvia Milano, Brent Mittelstadt, Sandra Wachter, Christopher Russell
2021 Nature Machine Intelligence  
Instead, regulators should promote an active role for consumers in fighting epistemic fragmentation by adopting a civic model of governance for advertising.  ...  Current methods adopted by the industry fall short of providing adequate explanations of actual targeting mechanisms 32 .  ...  acknowledgements This work of the Governance of Emerging Technologies research programme at the Oxford Internet Institute has been supported by British Academy Postdoctoral Fellowship grant number PF2\  ... 
doi:10.1038/s42256-021-00358-3 fatcat:wexszwiwlzcfjjulg7lwjz7wie

Context-Aware Recommendations with Random Partition Factorization Machines

Shaoqing Wang, Cuiping Li, Kankan Zhao, Hong Chen
2017 Data Science and Engineering  
We propose a Random Partition Factorization Machines (RPFM) by adopting random decision trees to split the contexts hierarchically to better capture the local complex interplay.  ...  In this work, we explore both of them so as to improve accuracy of prediction in recommender systems.  ...  In this work, we focus on collaborative filtering by exploiting the hierarchal information implied to improve the performance of recommendations.  ... 
doi:10.1007/s41019-017-0035-3 fatcat:4mzfjedborcu7o6yqd36ak2fsa

Computational advertising

Kushal S. Dave
2011 Proceedings of the 20th international conference companion on World wide web - WWW '11  
Contextual advertising deals with matching advertisements to the third party web pages.  ...  Based on the context, CA can be broadly compartmentalized into following three areas: Sponsored search, Contextual advertising and Social advertising.  ...  Recommending a set of ads to a user can be formulated as a problem of collaborative filtering (CF).  ... 
doi:10.1145/1963192.1963342 dblp:conf/www/Dave11 fatcat:vc6rwvzoorgo5csswgsqy6oupe

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Dina Nawara, Rasha Kashef
2021 IEEE Access  
Context-Aware recommenders are different from traditional recommenders because of their ability to predict the ratings of target users/items by exploiting the knowledge of contextual information.  ...  With the rapid growth of IoT-connected sensors, the availability of contextual information has increased, and this has necessitated the fast development of Context-Aware Recommendation Systems (CARS).  ...  Most of the literature adopted a contextual pre-filtering approach [9] , [30] .  ... 
doi:10.1109/access.2021.3122098 fatcat:oou7v6nyindovjwixfvyf43jiq

Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation [article]

Weinan Zhang, Lingxi Chen, Jun Wang
2016 arXiv   pre-print
delivers the relevant ads according to each user's interest, which leads to higher targeting accuracy and thus more improved advertising performance.  ...  Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users' interest profiles via tracking their online behaviour and then  ...  Collaborative Filtering (CF) on the other hand is a technique for personalised recommendation [26] .  ... 
arXiv:1601.02377v1 fatcat:iwe3442jcbh3bp4znxelkwnfzm

Differential Privacy and Bayesian for Context-Aware Recommender Systems

Shuxin Yang, Kaili Zhu
2021 International Journal of Cognitive Informatics and Natural Intelligence  
And then adopts Bayesian Network technology to calculate the probability that users like a type of item with contextual information.  ...  Incorporate contextual information into recommendation systems can obtain better accuracy of recommendation, however, the users' individual privacy may be disclosed by attackers.  ...  ACKNowLedGMeNT The research was supported by National Natural Science Foundation of China [grant number 61662028]; Science Foundation of Jiangxi Educational Committee [grant number GJJ170518] and Innovation  ... 
doi:10.4018/ijcini.20211001.oa2 fatcat:6kmfgqk5jvdlhj2anin3umq2xu

E-Commerce Business Models in the Context of Web 3.0 Paradigm

Fernando Almeida, Jose D. Santos, Jose A. Monteiro
2013 International Journal of Advanced Information Technology  
Additionally, semantic technologies have the potential to drive significant improvements in capabilities and life cycle economics through cost reductions, improved efficiencies, enhanced effectiveness,  ...  As a consequence, contextual advertisers can sell targeted advertising based on an individual user's surfing activity.  ...  Customers collaborate with businesses directly or indirectly to improve products, services, and the customer experience.  ... 
doi:10.5121/ijait.2013.3601 fatcat:b5frls42ezarrhqxbwsfalggne

Functionality-Based Service Matchmaking for Service-Oriented Architecture

Stephen S. Yau, Junwei Liu
2007 Eighth International Symposium on Autonomous Decentralized Systems (ISADS'07)  
This approach utilizes SAW-OWL-S to specify the service advertisements and service discovery requests.  ...  This approach uses functionality filtering to prune out incompatible services, and then select services based on the aggregated similarities of input/output parameters, precondition/result situations and  ...  Acknowledgment The work reported here was supported by the National Science Foundation under grant number ITR-CYBERTRUST 0430565.  ... 
doi:10.1109/isads.2007.39 dblp:conf/isads/YauL07 fatcat:sx3jdsf5zzbzvpb3xbnsbnmulm
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