Product Recommendation using Image and Text Processing

Kawattikul, Khanabhorn

doi:10.23919/incit.2018.8584860

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…High-quality product pictures can significantly enhance consumers' trust in word-of-mouth information, interest in products, and purchase intention [15,16]. This is because product pictures can often shape consumers' initial impressions of products for the first time and provide detailed product displays by simulating real items, helping consumers to have a more comprehensive understanding of all the details of products, thus improving consumers' interaction efficiency and having a profound impact on their purchase decisions [17][18][19][20].…”

Section: Image Information and Text Informationmentioning

confidence: 99%

“…Studies have shown that when product pictures are combined with relevant text descriptions, consumers tend to remember more details and understand and remember product information in a more profound way [19]. In addition, the study of Necula et al [20] also revealed an important correlation: consumers' interest in additional verbal descriptions of product functions, the importance of product functions, and consumer satisfaction are closely linked. They found that the level of this interest not only depends on the importance of product features but is also closely related to the consumer's perception of product satisfaction.…”

Section: Image Information and Text Informationmentioning

confidence: 99%

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Impact of Text and Image Information on Community Group Buying Performance: Empirical Evidence from Convenience Chain Stores

Liu,

Yan,

Tian

et al. 2024

Sustainability

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Although the importance of the content of a webpage in retail business performance is widely recognized, there are few empirical studies on the importance of text and image information on the homepage in retailer performance. How will consumers sift through this information? Does text and image information affect consumers’ purchasing behavior? Using a data set of a Chinese convenience chain store, we attempt to clarify the influence of the brand in the title, the emotional atmosphere on the picture, and the product images of the homepage on the picture on retail business performance by employing a panel fixed-effects negative binomial regression model and a panel fixed-effect regression. Our results show that mentioning the product brand in the event title and presenting clear product images significantly enhance retailer performance in online community group buying. It is noteworthy that emotional descriptions have a greater impact on retailer performance compared to rational descriptions. In practice, this study provides a new perspective and reference for online community group buying platforms to better attract consumers and maintain sustainable development.

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Section: Image Information and Text Informationmentioning

confidence: 99%

Section: Image Information and Text Informationmentioning

confidence: 99%

Impact of Text and Image Information on Community Group Buying Performance: Empirical Evidence from Convenience Chain Stores

Liu,

Yan,

Tian

et al. 2024

Sustainability

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“…In ecommerce websites, almost all items are displayed intuitively as images. For a consumer to purchase the needed product, the key determinant is the accurate retrieval of product images with a high similarity [17][18][19]. In terms of product retrieval, Moorthy et al [20] put forward a target retrieval algorithm for cross-domain beauty products based on multi-attentional mechanism, which realizes accurate retrieval of real beauty products in two steps: all candidate box features of the products were characterized by channel weighted generalized mean pooling features, and the attention mechanism was adopted to learn the description of finegrained features.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Retrieval of Ecommerce Product Images Based on Image Processing

Wei¹,

Zhang²

2021

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The new retail is an industry featured by online ecommerce. One of the key techniques of the industry is the product identification based on image processing. This technique has an important business application value, because it is capable of improving the retrieval efficiency of products and the level of information supervision. To acquire high-level semantics of images and enhance the retrieval effect of products, this paper explores the feature extraction and retrieval of ecommerce product images based on image processing. The improved Fourier descriptor was innovatively into a metric learning-based product image feature extraction network, and the attention mechanism was introduced to realize accurate retrieval of product images. Firstly, the authors detailed how to acquire the product contour and the axis with minimum moment of inertia, and then extracted the shape feature of products. Next, a feature extraction network was established based on the metric learning supervision, which is capable of obtaining distinctive feature, and thus realized the extraction of distinctive and classification features of products. Finally, the authors expounded on the product image retrieval method based on cluster attention neural network. The effectiveness of our method was confirmed through experiments. The research results provide a reference for feature extraction and retrieval in other fields of image processing.

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