Top

Soft Computing

Published in:

26-02-2021 | Methodologies and Application

An image recommendation technique based on fuzzy inference system

Authors: Somaye Ahmadkhani, Mohsen Ebrahimi Moghaddam

Published in: Soft Computing | Issue 8/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Nowadays, social network users are facing with a large number of images with the fast development in the social image sharing media. In this situation, finding the images which can satisfy user preferences is more important. Since users' liking behavior has not a certain manner, it can be modeled by fuzzy logic because fuzzy logic is a decision-making system based on degrees of truth and deals with vague and imprecise information. Therefore, in the present study, we proposed a fuzzy inference system based on user preferences, consisting of a set of rules according to distinguishing features for each user. Also together with fuzzy inference system, this method is proposed based on feature extraction and feature selection in order to predict the images which are liked by different users. In the proposed method, the features from user preferred images are extracted. Then, feature selection is used to determine the most discriminant features for each user, and accordingly fuzzy inference system is employed to learn user preferences. We used fuzzy clustering to generate soft clusters and fuzzify user preferences. Then, this system is used to predict images that should be recommended to the user. Experimental results on datasets from Flickr and Pinterest indicated that the proposed method can significantly improve Recall@k and Precision@k for personalized image recommendation compared to some related works.

previous article Optimal type-2 fuzzy synchronization of two different fractional-order chaotic systems with variable orders with an application to secure communication

next article Improved multiobjective bat algorithm for the credibilistic multiperiod mean-VaR portfolio optimization problem

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Al-Shamri MYH, Al-Ashwal NH (2014) Fuzzy-weighted similarity measures for memory-based collaborative recommender systems. J Intell Learn Syst Appl, vol. 2014

Alashkar T, Jiang S, Wang S, Fu Y (2017) Examples-rules guided deep neural network for makeup recommendation. In: AAAI, pp. 941–947

Albarracin D, Wyer RS Jr (2000) The cognitive impact of past behavior: influences on beliefs, attitudes, and future behavioral decisions. J Pers Soc Psychol 79:5CrossRef

Batmaz Z, Yurekli A, Bilge A, Kaleli C (2019) A review on deep learning for recommender systems: challenges and remedies. Artif Intell Rev 52:1–37CrossRef

Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132CrossRef

Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), pp 886–893

Fonti V, Belitser E (2017) Feature selection using lasso. VU Amst Res Paper Bus Anal 30:1–25

Geng X, Zhang H, Bian J, Chua T-S (2015) Learning image and user features for recommendation in social networks. In: Proceedings of the IEEE international conference on computer vision, pp 4274–4282.

Gomez JC, Ibarra-Manzano M-A, Almanza-Ojeda D-L (2017) User identification in pinterest through the refinement of a cascade fusion of text and images. Adv Lang Knowl Eng 144:40

Guntuku SC, Zhou JT, Roy S, Lin W, Tsang IW (2016) Understanding deep representations learned in modeling users likes. IEEE Trans Image Process 25:3762–3774MathSciNetCrossRef

Guntuku SC, Roy S, Weisi L (2015) Personality modeling based image recommendation. In: International conference on multimedia modeling, pp 171–182

He R, McAuley J (2016) Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th international conference on world wide web, pp 507–517

He R, McAuley J (2015) VBPR: visual bayesian personalized ranking from implicit feedback. arXiv preprint https://arxiv.org/abs/1510.01784.

Jian M, Jia T, Wu L, Zhang L, Wang D (2020) Content-based bipartite user-image correlation for image recommendation. Neural Process Lett 52:1445–1459CrossRef

Kanimozhi U, Ganapathy S, Manjula D, Kannan A (2019) An intelligent risk prediction system for breast cancer using fuzzy temporal rules. Natl Acad Sci Lett 42:227–232CrossRef

Katarya R, Verma OP (2018) Recommender system with grey wolf optimizer and FCM. Neural Comput Appl 30:1679–1687CrossRef

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

Lazzez O, Ouarda W, Alimi AM (2018) DeepVisInterests: CNN-Ontology Prediction of users interests from social images. arXiv preprint https://arxiv.org/abs/1811.10920,

Lei C, Liu D, Li W, Zha Z-J, Li H (2016) Comparative deep learning of hybrid representations for image recommendations. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2545–2553

Li H, Shen Y, Wang C, Yang H, Ali R (2017) Reading behaviour based user interests model and its application in recommender system. In 2017 IEEE 2nd advanced information technology, electronic and automation control conference (IAEAC), pp 683–690

Liu S, Cui P, Zhu W, Yang S, Tian Q (2014) Social embedding image distance learning. In: Proceedings of the 22nd ACM international conference on multimedia, pp. 617–626

Lovato P, Bicego M, Segalin C, Perina A, Sebe N, Cristani M (2014) Faved! biometrics: tell me which image you like and I’ll tell you who you are. IEEE Trans Inf Forensics Secur 9:364–374CrossRef

Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60:91–110CrossRef

Mahendran A, Vedaldi A (2015) Understanding deep image representations by inverting them. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5188–5196

Manogaran G, Varatharajan R, Priyan M (2018) Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimed Tools Appl 77:4379–4399CrossRef

Martinez L, Barranco MJ, Pérez LG, Espinilla M (2008) A knowledge based recommender system with multigranular linguistic information. Int J Comput Intell Syst 1:225–236CrossRef

Martínez L, Pérez LG, Barranco M (2007) A multigranular linguistic content-based recommendation model. Int J Intell Syst 22:419–434CrossRef

MathWorks T (2006) Fuzzy logic toolbox user's guide

Melin P, Mendoza O, Castillo O (2010) An improved method for edge detection based on interval type-2 fuzzy logic. Expert Syst Appl 37:8527–8535CrossRef

Niu W, Caverlee J, Lu H (2018) Neural personalized ranking for image recommendation. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp. 423–431

Özkan K, Kurt Z, Seke E (2019) An image-based recommender system based on image annotation. Eur J Eng Nat Sci 3:12–16

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115:211–252MathSciNetCrossRef

Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint https://arxiv.org/abs/1409.1556

Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell, vol 2009

Sulthana A, Gupta M, Subramanian S, Mirza S (2020) Improvising the performance of image-based recommendation system using convolution neural networks and deep learning. Soft Comput 24:1–14CrossRef

Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, pp 116–132

Tan M, Tsang IW, Wang L (2014) Towards ultrahigh dimensional feature selection for big data. J Mach Learn Res 15:1371–1429MathSciNetMATH

Tan M, Wang L, Tsang IW (2010) Learning sparse SVM for feature selection on very high dimensional datasets. In: ICML

Vijay Kumar T, Lavanya N, Khanna N, Ganapathy S, Kannan A (2018) Identification and classification of pulmonary nodule in lung modality using digital computer. Appl Math Inf Sci 12:451–459CrossRef

Vijilious ML, Ganapathy S, Bharathi VS (2012) Palmprint feature extraction approach using nonsubsampled contourlet transform and orthogonal moments. In: Proceedings of the international conference on advances in computing, communications and informatics, pp 735–739

Wang D, He T, Li Z, Cao L, Dey N, Ashour AS et al (2018) Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system. Neural Comput Appl 29:1087–1102CrossRef

Wang Y, Lin X, Wu L, Zhang W (2017) Effective multi-query expansions: collaborative deep networks for robust landmark retrieval. IEEE Trans Image Process 26:1393–1404MathSciNetCrossRef

Wu L, Chen L, Hong R, Fu Y, Xie X, Wang M (2019) A hierarchical attention model for social contextual image recommendation. IEEE Trans Knowl Data Eng

Yera R, Martinez L (2017) Fuzzy tools in recommender systems: a survey. Int J Comput Intell Syst 10:776–803CrossRef

Yu L, Han F, Huang S, Luo Y (2018) A content-based goods image recommendation system. Multimed Tools Appl 77:4155–4169CrossRef

Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision, pp 818–833

Zenebe A, Norcio AF (2009) Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst 160:76–94MathSciNetCrossRef

Zhang R, Nie F, Li X, Wei X (2019b) Feature selection with multi-view data: a survey. Inf Fusion 50:158–167CrossRef

Zhang H, Shang X, Luan H, Wang M, Chua T-S (2016) Learning from collective intelligence: Feature learning using social images and tags. ACM Trans Multimedia Comput Commun Appl 13:1–23

Zhang J, Yang Y, Tian Q, Zhuo L, Liu X (2017) Personalized social image recommendation method based on user-image-tag model. IEEE Trans Multimedia 19:2439–2449CrossRef

Zhang J, Yang Y, Zhuo L, Tian Q, Liang X (2019) Personalized recommendation of social images by constructing a user interest tree with deep features and tag trees. IEEE Trans Multimedia 21:2762–2775CrossRef

Zhang S, Yao L, Sun A, Tay Y (2019a) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 52:1–38

Zhang Y, Ai Q, Chen X, Croft WB (2017) Joint representation learning for top-n recommendation with heterogeneous information sources. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1449–1458

Title: An image recommendation technique based on fuzzy inference system
Authors: Somaye Ahmadkhani
Mohsen Ebrahimi Moghaddam
Publication date: 26-02-2021
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 8/2021
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-021-05637-0

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 8/2021

Fuzzy-based multiparty privacy management in social media using modified elliptic curve cryptography

Re-design of a blood supply chain organization with mobile units

Wind speed multistep forecasting model using a hybrid decomposition technique and a selfish herd optimizer-based deep neural network

Bayesian inference for infinite asymmetric Gaussian mixture with feature selection

Integrating virtual sample generation with input-training neural network for solving small sample size problems: application to purified terephthalic acid solvent system

Mobile robot path planning with obstacle avoidance using chemical reaction optimization

Premium Partner