• Journal of Fashion Business
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• v.26 no.2
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• pp.1-14
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• 2022

Recently, as the importance of mobile marketing is emphasized, the in-app advertising market, which inserts ads into applications, is growing. The purpose of this study is to verify the interaction effects of need for cognition, contextual consistency, and product involvement on advertising attitude. The experimental design of this study is a three-way mixed design of 2 (consumer need for cognition: high vs low) × 2 (contextual consistency: context match vs context mismatch) × 2 (product involvement: high vs low). The subjects of the survey were 337 men and women in their 20s and 30s Living in Seoul and Gyeonggi-do. SPSS 25.0 statistical program was used to analyze frequency analysis, reliability analysis, t-test, three-way ANOVA, and simple main effect analysis. The analysis results are as follows. First, contextual consistency of mobile fashion ads showed significant effect on advertising attitude. Second, consumer need for cognition and contextual consistency of mobile fashion ads showed significant interaction effect on advertising attitude. Third, contextual consistency of mobile fashion ads and product involvement showed significant interaction effect on advertising attitude. Finally, product involvement and consumer need for cognition showed a significant interaction effect on advertising attitude. Based on the research results, it will help fashion companies to establish effective mobile in-app advertising strategies.

• ETRI Journal
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• v.34 no.2
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• pp.292-295
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• 2012

Beyond the simple keyword matching methods in contextual advertising, we propose a rich contextual matching (CM) model adopting a classification method for topic targeting and a query expansion method for semantic ad matching. This letter reports on an investigation into the empirical effects of the CM model by comparing the click-through rates (CTRs) of two practical online news advertising systems. Based on the evaluation results from over 100 million impressions, we prove that the average CTR of our proposed model outperforms that of a traditional model.

• Journal of Information Technology Applications and Management
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• v.19 no.4
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• pp.115-135
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• 2012

Currently, smart signage as a new kind of advertisement media has been widely used and has had a significant effect on consumers. In this study, we examine the impact on the attitude towards smart signage advertisement based on the Ducoffe's Web advertisement model. In particular, we investigate how the advertising factors (interaction, informativeness, irritation, and entertainment) as well as the contextual factors (localization, immediacy, and personalization) have effects on the value and attitude towards smart signage advertisement. Furthermore, we examine the moderating effect of individual defensive propensity on the relationship between affecting factors and the advertising value. By analyzing 192 samples from undergraduates, we found that four out of seven factors have significant effects on the value towards smart signage. More specifically, for advertising factors, informativeness and irritation have positive effects on the advertising value. For contextual factors, on the other hand, localization and entertainment have positive effects on the value towards smart signage. Besides, we discovered that the defensive propensity moderate the relationship between localization and the advertising value.

• International Journal of Contents
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• v.7 no.1
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• pp.37-44
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• 2011

This research attempts to analyze brand placement in game. Brand placement, being acclaimed as a new beneficiary model in game industry, is raising important mean of advertising. For development of game industry, the interdisciplinary study between game and advertising is indispensible. Therefore, the purpose of this study is to find creative types of brand placement in game for illuminating how advertising works in game contents. The results showed three types of brand placement in game. They are contextual type, prominent type and independent type. Contextual type is one where the brand is present within the game contents without being formally expressed: it plays a passive role. Prominent type is one where the brand is present within the game contents with being formally expressed: it plays an active role. Independent type is one where the brand is present within the game contents with being formally expressed but it is not related with the program: it plays an additional role. The research showed, among these three types, a prominent type is becoming mainstream of brand placement in game. In other words, the prominent type of brand placement is the most effective beneficial alternative in game industry.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.95-117
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• 2022

With the emergence of various media types due to the development of Internet technology, advertisers have difficulty choosing media suitable for corporate advertising strategies. There are challenging to effectively reflect consumer contextual information when advertising media is selected based on traditional marketing strategies. Thus, a recommender system is needed to analyze consumers' past data and provide advertisers with personalized media based on the information consumers needs. Since the traditional recommender system provides recommendation services based on quantitative preference information, there is difficult to reflect various contextual information. This study proposes a methodology that uses deep learning to recommend personalized media to advertisers using consumer contextual information such as consumers' media viewing time, residence area, age, and gender. This study builds a recommender system using media & consumer research data provided by the Korea Broadcasting Advertising Promotion Corporation. Additionally, we evaluate the recommendation performance compared with several benchmark models. As a result of the experiment, we confirmed that the recommendation model reflecting the consumer's contextual information showed higher accuracy than the benchmark model. We expect to contribute to helping advertisers make effective decisions when selecting customized media based on various contextual information of consumers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1373-1387
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• 2012

Contextual advertising is an important revenue source for major service providers on the Web. Ads classification is one of main tasks in contextual advertising, and it is used to retrieve semantically relevant ads with respect to the content of web pages. However, it is difficult for traditional text classification methods to achieve satisfactory performance in ads classification due to scarce term features in ads. In this paper, we propose a novel ads classification method that handles the lack of term features for classifying ads with short text. The proposed method utilizes a vocabulary expansion technique using semantic associations among terms learned from large-scale search query logs. The evaluation results show that our methodology achieves 4.0% ~ 9.7% improvements in terms of the hierarchical f-measure over the baseline classifiers without vocabulary expansion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.3036-3044
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• 2010

With the rapid growth of video data service, demand to provide advertisements or additional information with regard to a particular video scene is increasing. However, the direct use of automated visual analysis or speech recognition on videos virtually has limitations with current level of technology; the metadata of video such as title, category information, or summary does not reflect the content of continuously changing scenes. This work presents a new video contextual advertising system that serves relevant advertisements on a given scene by leveraging the scene's situation information inferred from video scripts. Experimental results show that the use of situation information extracted from scripts leads to better performance and display of more relevant advertisements to the user.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1052-1070
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• 2017

For the autoencoder (AE) implemented as a construction component, this paper uses the method of greedy layer-by-layer pre-training without supervision to construct the stacked autoencoder (SAE) to extract the abstract features of the original input data, which is regarded as the input of the logistic regression (LR) model, after which the click-through rate (CTR) of the user to the advertisement under the contextual environment can be obtained. These experiments show that, compared with the usual logistic regression model and support vector regression model used in the field of predicting the advertising CTR in the industry, the SAE-LR model has a relatively large promotion in the AUC value. Based on the improvement of accuracy of advertising CTR prediction, the enterprises can accurately understand and have cognition for the needs of their customers, which promotes the multi-path development with high efficiency and low cost under the condition of internet finance.

• Archives of design research
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• v.16 no.3
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• pp.273-280
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• 2003

Priming Effect, as a part of "Contextual Effect" is the phenomenon that pre-searched product information influences consecutive product evaluation. "Priming Effect" is different from other types of "Contextual Effects" in that pre-searched information doesn't have an direct effect on product evaluation, but an indirect effect which means that pre-searched information influences interpretation of consecutive product evaluation. On the previous theoretical background, this study investigated that how visual images can influence consumer preference and product choice, introducing concept of priming effects into the design of on-line shopping malls. This experiment adopted the digital cameras as the experimental stimulus target products and designed the individual web pages by priming the attributes of price and size. In result of analysis, the difference of product preference by priming attributes was proved to be statistically significant. In this reason, suggesting the background of commercial web page can be the significant factor in choosing products, the priming effects introduced into the design can be positively employed in online advertising.mployed in online advertising.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2122-2140
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• 2020

Proper and precise embedding of commercial ads within Webpages requires Ad-hoc analysis and understanding of their content. By the successful implementation of this step, both publishers and advertisers gain mutual benefits through increasing their revenues on the one hand, and improving user experience on the other. In this research work, we propose a novel multi-level context-based ads serving approach through which ads will be served at generic publisher websites based on their contextual relevance. In the proposed approach, knowledge encoded in domain-specific and generic semantic repositories is exploited in order to analyze and segment Webpages into sets of contextually-relevant segments. Semantically-enhanced indexes are also constructed to index ads based on their textual descriptions provided by advertisers. A modified cosine similarity matching algorithm is employed to embed each ad from the Ads repository into one or more contextually-relevant segments. In order to validate our proposal, we have implemented a prototype of an ad serving system with two datasets that consist of (11429 ads and 93 documents) and (11000 documents and 15 ads), respectively. To demonstrate the effectiveness of the proposed techniques, we experimentally tested the proposed method and compared the produced results against five baseline metrics that can be used in the context of ad serving systems. In addition, we compared the results produced by our system with other state-of-the-art models. Findings demonstrate that the accuracy of conventional ad matching techniques has improved by exploiting the proposed semantically-enhanced context-based ad serving model.