An Image-based Recommender System Based on Image Annotation

Year 2019, Volume: 3 Issue: 1, 12 - 16, 27.06.2019

Kemal Özkan , Zühal Kurt , Erol Seke

Abstract

Recommender system is a software that analyzes available data to make recommendations about various
products and services to their users might be interested in.
These systems must perform efficient  for
both users and the e-commerce sites benefiting 
from such systems. Ensuring proper and reliable recommendations
increases user satisfaction that results selling more products and services. Collaborative
filtering, content-based, and hybrid techniques are type of methods for recommender
systems. Content-based recommender systems are usually text-based systems, but
image-based recommender systems have become increasingly in favour for the content-based recommender
systems in recent years.   

The process of an image based recommender system is
to match a users’ image with the most similar image and recommend it. The
recommendation images are the most likely images uploaded and widely acclaimed
from the users. The most challenging problem in image-based recommneder systems
is to match an image with the most similar visual words or classes based on the
image’s visual content.  In this study,
we are planning to solve this problem using Bag of words model, which is an
effective model in computer vision, and also features are extracted with
commonly used descriptors.

Keywords

Bag of words model, descriptor, cosine similarity, Image-based recommender system

References

• [1]. K. Pliakos, and C. Kotropoulos, “Building an image annotation and tourism recommender system”, Int. J. Artif. Intell. Tools, 24 (5), 2015.
• [2]. A. Sasa, M. Krisper, Y. Kiyoki, and X. Chen, “A personalized recommender system model using colour-impression-based image retrieval and ranking method”, ICIW 2011 : The Sixth International Conference on Internet and Web Applications and Services, 124-130, 2011.
• [3]. J. Fan, D.A. Keim, Y. Gao, H. Luo, and Z. Li, “JustClick: Personalized image recommendation via exploratory search from large-scale flickr image collections”, IEEE Trans. Circuits Syst. Video Technol., 19 (2): 273-288, 2008.
• [4]. P. Bhagat, N. Mahakalkar, R. Chaudhari, and A. Gotmare, “A survey paper on profile- based image recommender system for smartphone”, In International Journal of Engineering Research and Technology, vol(3), ESRSA Publications, 2014.
• [5]. L. Cao, J. Luo, A. Gallagher, X. Jin, J. Han, and T.S. Huan, “A worldwide tourism recommendation system based on geotaggedweb photos”, IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2274-2277, 2010.
• [6]. D. G. Lowe, “Distinctive image features from scale-invariant keypoints”, Int. J.Comput. Vision, 60: 91-110, 2004.
• [7]. S. Van de, E.A. Koen, T. Gevers, and G. M.C. Snoek, “Evaluating color descriptors for object and scene recognition”, IEEE T. Pattern Anal., 32 (9):1582-1596, 2010.
• [8]. H. Bay, T. Tuytelaars, and L.V. Gool, “SURF: Speeded up robust features”, In ECCV, (1): 404-417, 2006.
• [9]. J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust wide baseline stereo from maximally stable extremal regions”, Proc. of British Machine Vision Conference, 384-396, 2002.
• [10]. M. Heikkila, M. Pietikainen, and C. Schmid, “Description of interest regions with local binary patterns”, Pattern Recogn. , 42: 425-436, 2009.
• [11]. https://webscope.sandbox.yahoo.com/catalog.php?datatype=i.