Automatic tag expansion using visual similarity for photo sharing websites

Sevil, S.; Küçüktunç, Onur; Duygulu, Pınar; Can, Fazlı

doi:10.1007/s11042-009-0394-5

Cited by 20 publications

(8 citation statements)

References 24 publications

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“…Hybrid methods can be further divided into two categories: loosely and tightly coupled methods. Loosely coupled methods [Melville et al, 2002;Sevil et al, 2010] make the use of auxiliary information to provide features for CF but the rating information cannot guide the learning of features. On the contrary, tightly coupled methods [Wang and Blei, 2011;Wang et al, 2015] provide two-way interaction between them and often outperform loosely coupled ones.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Item-Item Recommendation via Semi-Parametric Embedding

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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Nowadays, item-item recommendation plays an important role in modern recommender systems. Traditionally, this is either solved by behavior-based collaborative filtering or content-based meth- ods. However, both kinds of methods often suffer from cold-start problems, or poor performance due to few behavior supervision; and hybrid methods which can leverage the strength of both kinds of methods are needed. In this paper, we propose a semi-parametric embedding framework for this problem. Specifically, the embedding of an item is composed of two parts, i.e., the parametric part from content information and the non-parametric part designed to encode behavior information; meanwhile, a deep learning algorithm is proposed to learn two parts simultaneously. Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Hybrid Item-Item Recommendation via Semi-Parametric Embedding

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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“…For example, the works of Papadopoulos et al [2010] and Sevil et al [2010] apply clustering methods to achieve tag propagation. Other approaches exploit the content of items (e.g., content of images, songs, etc.)…”

Section: Idmentioning

confidence: 99%

Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging

Rafailidis

Αξενόπουλος

Etzold

et al. 2014

ACM Trans. Interact. Intell. Syst.

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In this article, a novel method for personalized item recommendation based on social tagging is presented. The proposed approach comprises a content-based tag propagation method to address the sparsity and "cold start" problems, which often occur in social tagging systems and decrease the quality of recommendations. The proposed method exploits (a) the content of items and (b) users' tag assignments through a relevance feedback mechanism in order to automatically identify the optimal number of content-based and conceptually similar items. The relevance degrees between users, tags, and conceptually similar items are calculated in order to ensure accurate tag propagation and consequently to address the issue of "learning tag relevance." Moreover, the ternary relation among users, tags, and items is preserved by performing tag propagation in the form of triplets based on users' personal preferences and "cold start" degree. The latent associations among users, tags, and items are revealed based on a tensor factorization model in order to build personalized item recommendations. In our experiments with real-world social data, we show the superiority of the proposed approach over other state-of-the-art methods, since several problems in social tagging systems are successfully tackled. Finally, we present the recommendation methodology in the multimodal engine of I-SEARCH, where users' interaction capabilities are demonstrated. ACM Reference Format:Dimitrios Rafailidis, Apostolos Axenopoulos, Jonas Etzold, Stavroula Manolopoulou, and Petros Daras. 2014. Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging.

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“…Using the priori data mining algorithm the support/confidence object association was estimated and the result is shown in table [1]. In This table we show only the occurrence of objects that contains association rule with support over 0.002and confidence over 0.1.Categories such as dog , cat and horse gain significantly from context.…”

Section: Semantic Based Object Annotationmentioning

confidence: 99%

“…however, the manual tags on the images websites suffer from significant problems, such as incomplete ("missing") for tags, misspelling, noisy ("incorrect") tags ,and subjectivity and time consuming [1], [2]. To reduce these problems, automatic image annotation has received a lot of attention recently.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Object Annotation through Context Analysis

Riad¹,

Elminir²,

Abdel-Ghany³

2013

IJAIS

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The goal of object level annotation is to locate and identify instances of an object category within an image. Nowadays, Most of the current object level annotation systems annotate the object according to the visual appearance in the image. Recognizing an object in an image based visual appearance yield ambiguity in object detection due to appearance confusion for example "sky" object may be annotated as "water" according to similarity in visual appearance. As a result, these systems don't recognize the objects in an image accurately due to the lack of scene context. In the task of visual object recognition, scene context can play important role in resolving the ambiguities in object detection. In order to solve the ambiguity problem, this paper presents a new technique for a context based object level annotation that considers both the semantic context and spatial context analysis to reduce ambiguous in object annotation.

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Automatic tag expansion using visual similarity for photo sharing websites

Cited by 20 publications

References 24 publications

Hybrid Item-Item Recommendation via Semi-Parametric Embedding

Hybrid Item-Item Recommendation via Semi-Parametric Embedding

Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging

Unsupervised Object Annotation through Context Analysis

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