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Статті в журналах з теми "Product search"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Knöferle, Klemens, and Charles Spence. "Product-related sounds speed visual search." Seeing and Perceiving 25 (2012): 193. http://dx.doi.org/10.1163/187847612x648224.

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Анотація:

Searching for a particular product in a supermarket can be a challenging business. The question therefore arises as to whether cues from the shopper’s other senses can be used to facilitate, guide, or bias visual search toward a particular product or product type. Prior research suggests that characteristic sounds can facilitate visual object localization (Iordanescu et al., 2008, 2010). Extending these findings to an applied setting, we investigated whether product-related sounds would facilitate visual search for products from different categories (e.g., champagne, potato crisps, deodorant) when arranged on a virtual shelf. On each trial, participants were visually presented with the name of a target product and then located the target within a virtual shelf display containing pictures of four different products (randomly selected from a set of nine). The visual display was randomly accompanied by a target-congruent, a target-incongruent, an unrelated, or no sound. Congruent sounds were semantically related to the target (e.g., uncorking a champagne bottle), incongruent sounds were related to the product shown in the corner opposite to the target, and unrelated sounds did not correspond to any of the products shown in the display. Participants found the target product significantly faster when the sound was congruent rather than incongruent with the target. All other pairwise comparisons were non-significant. These results extend the facilitatory crossmodal effect of characteristic sounds on visual search performance described earlier to the more realistic context of a virtual shelf display, showing that characteristic sounds can crossmodally enhance the visual processing of actual products.

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2

Thong, William, and Cees G. M. Snoek. "Diversely-Supervised Visual Product Search." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1 (January 31, 2022): 1–22. http://dx.doi.org/10.1145/3461646.

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Анотація:

This article strives for a diversely supervised visual product search, where queries specify a diverse set of labels to search for. Where previous works have focused on representing attribute, instance, or category labels individually, we consider them together to create a diverse set of labels for visually describing products. We learn an embedding from the supervisory signal provided by every label to encode their interrelationships. Once trained, every label has a corresponding visual representation in the embedding space, which is an aggregation of selected items from the training set. At search time, composite query representations retrieve images that match a specific set of diverse labels. We form composite query representations by averaging over the aggregated representations of each diverse label in the specific set. For evaluation, we extend existing product datasets of cars and clothes with a diverse set of labels. Experiments show the benefits of our embedding for diversely supervised visual product search in seen and unseen product combinations and for discovering product design styles.

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3

Thakare, Abhijeet Ramesh, and Parag S. Deshpande. "Comparative Search of Entities." International Journal of Software Engineering and Knowledge Engineering 27, no. 08 (October 2017): 1333–57. http://dx.doi.org/10.1142/s0218194017500498.

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Анотація:

Nowadays, every manufacturer or retailer displays their product information on various websites. The customer has to visit, the number of such web pages to choose the right product, because the information is not available at one place. There are some websites that show such information in one place, but they are product specific and in general information is manually updated. In this paper, we propose a novel concept of web-spreadsheet, which displays product information by crawling through related web pages and generates information like a spreadsheet where each row represents product information and each column represents product attributes. We are extracting the product name of specified product class using decision tree-based classifier by features obtained using Part of Speech (POS) tagging and distance measure. It also extracts the value-measure pairs of preset attributes using distance measure, POS tagging and Data type. This approach will save a lot of time of comparing different products and customers need not have to scan a number of websites for comparison. We present promising results in various product classes which surpass many existing techniques in the literature. The proposed method can work accurately without initial trained labeled data which is expensive to obtain.

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4

Pan, Yaoxin, Shangsong Liang, Jiaxin Ren, Zaiqiao Meng, and Qiang Zhang. "Personalized, Sequential, Attentive, Metric-Aware Product Search." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–29. http://dx.doi.org/10.1145/3473337.

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The task of personalized product search aims at retrieving a ranked list of products given a user’s input query and his/her purchase history. To address this task, we propose the PSAM model, a Personalized, Sequential, Attentive and Metric-aware (PSAM) model, that learns the semantic representations of three different categories of entities, i.e., users, queries, and products, based on user sequential purchase historical data and the corresponding sequential queries. Specifically, a query-based attentive LSTM (QA-LSTM) model and an attention mechanism are designed to infer users dynamic embeddings, which is able to capture their short-term and long-term preferences. To obtain more fine-grained embeddings of the three categories of entities, a metric-aware objective is deployed in our model to force the inferred embeddings subject to the triangle inequality, which is a more realistic distance measurement for product search. Experiments conducted on four benchmark datasets show that our PSAM model significantly outperforms the state-of-the-art product search baselines in terms of effectiveness by up to 50.9% improvement under NDCG@20. Our visualization experiments further illustrate that the learned product embeddings are able to distinguish different types of products.

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5

Branco, Fernando, Monic Sun, and J. Miguel Villas-Boas. "Optimal Search for Product Information." Management Science 58, no. 11 (November 2012): 2037–56. http://dx.doi.org/10.1287/mnsc.1120.1535.

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6

Lu, Shiyang, Tao Mei, Jingdong Wang, Jian Zhang, Zhiyong Wang, and Shipeng Li. "Exploratory Product Image Search With Circle-to-Search Interaction." IEEE Transactions on Circuits and Systems for Video Technology 25, no. 7 (July 2015): 1190–202. http://dx.doi.org/10.1109/tcsvt.2014.2372272.

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7

Liu, Lin, and X. Henry Wang. "Product differentiation and equilibrium price with partial product search." Economics Letters 205 (August 2021): 109932. http://dx.doi.org/10.1016/j.econlet.2021.109932.

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8

Rozekhi, Nor Azureen, Shahril Hussin, and Ali Mohamad Noor. "Attributable E-commerce toward Purchase Intention: Online Search of Food Product." SIJ Transactions on Advances in Space Research & Earth Exploration 4, no. 1 (February 12, 2016): 6–12. http://dx.doi.org/10.9756/sijasree/v4i1/0203410401.

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9

Bourke, Dick. "Search …and find." Mechanical Engineering 135, no. 09 (September 1, 2013): 46–49. http://dx.doi.org/10.1115/1.2013-sep-3.

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Анотація:

This article focuses on different software tools that give engineers a quick access to product information. Software tools help access data generated during the product development process. Known as a search-based application or as unified information access, these tools use elements of semantic technology—machine-based recognition of meanings and relationships in text—to find information stored throughout a company’s multiple sources of data, including computer-aided design files and product lifecycle management systems. These software tools perform three functions: search, discover, and analyze. Search applications reduce the risks of using incomplete information when making product development decisions. Another type of search technology to consider is geometric-based search that pinpoints relevant parts based on shape. A company’s software selection criteria must encompass the informational needs of all product development activities throughout the enterprise. These activities include design engineering, manufacturing process planning, and quality control.

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10

Imam Farisi, Mukhaimy Gazali, and Rudy Anshari. "SQLite Sebagai Pengganti Lucene.Net pada Pencarian Produk Toko Online." Jurnal CoSciTech (Computer Science and Information Technology) 1, no. 2 (October 31, 2020): 36–43. http://dx.doi.org/10.37859/coscitech.v1i2.2204.

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Анотація:

A business institution can promote their products in its online shop. With the promotion of online stores, consumers can find out which products are sold at the store. Consumers who are looking for a product can search based on various attributes of goods that are scattered in various fields in the database table, and can even be spread across different tables. All attributes that point to an item are collected in one document which will be searched using the Full Text Search system. Two alternatives to the Full Text Search system were selected; Lucene.Net and Sqlite Full Text Search. Before actually being used, these two search system alternatives were tested first. In document storage size, Lucene.Net is superior by 6.78 times. The speed of writing Sqlite search documents is superior by between 1,875 times to 5,197 times. In terms of key search speed, Lucene.Net was superior by between 1,169 and 1,698 times. Based on the consideration of the speed and development of Lucene.Net Core which is still in beta stage, Sqlite Full Text Search is suitable for use in the product search process in the Online Store.

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11

Tse, Chung Yi. "New product introduction with costly search." Journal of Economic Dynamics and Control 30, no. 12 (December 2006): 2775–92. http://dx.doi.org/10.1016/j.jedc.2005.09.004.

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12

Song, Hui. "Ordered search with asymmetric product design." Journal of Economics 121, no. 2 (December 8, 2016): 105–32. http://dx.doi.org/10.1007/s00712-016-0518-0.

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13

Liu, Lin, and Anthony Dukes. "Consumer Search with Limited Product Evaluation." Journal of Economics & Management Strategy 25, no. 1 (December 10, 2015): 32–55. http://dx.doi.org/10.1111/jems.12131.

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14

Duan, Huizhong, ChengXiang Zhai, Jinxing Cheng, and Abhishek Gattani. "Supporting keyword search in product database." Proceedings of the VLDB Endowment 6, no. 14 (September 2013): 1786–97. http://dx.doi.org/10.14778/2556549.2556562.

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15

Jégou, H., M. Douze, and C. Schmid. "Product Quantization for Nearest Neighbor Search." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 1 (January 2011): 117–28. http://dx.doi.org/10.1109/tpami.2010.57.

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16

Luan, Jing, Zhong Yao, FuTao Zhao, and Hao Liu. "Search product and experience product online reviews: An eye-tracking study on consumers' review search behavior." Computers in Human Behavior 65 (December 2016): 420–30. http://dx.doi.org/10.1016/j.chb.2016.08.037.

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17

Dörnyei, Krisztina Rita, Athanasios Krystallis, and Polymeros Chrysochou. "The impact of product assortment size and attribute quantity on information searches." Journal of Consumer Marketing 34, no. 3 (May 8, 2017): 191–201. http://dx.doi.org/10.1108/jcm-10-2015-1594.

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Анотація:

Purpose This paper aims to investigate the impact of assortment size and attribute quantity on the depth and content of consumer information searches. Design/methodology/approach For a computer-aided experiment using an information display board, participants (n = 393) were placed in a simulated shopping situation that involved choosing a product among three sets of frequently purchased, low-involvement, FMCG alternatives. Findings The findings show that when the assortment size increases, consumers acquire information from more products and cues but sacrifice product attributes. In particular, this sacrifice comes at the expense of secondary product attributes (e.g. nutrition information, country of origin), whereas primary product attributes (e.g. brand name, price) remain constant. Attribute quantity does not have a significant effect on information search. Practical implications Provided that several strategies rely on providing more information to consumers with the aim of making more deliberate and better choices, the findings suggest that they may have a limited effect in product categories in which the assortment size is wide. The authors discuss the implications for category management and public policy. Originality/value Information searches are measured by means of three different variables (searched cues, searched products and searched attributes), which enable a more complex exploration of the consumer information search process.

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18

Goers, Bernd, Eveline Lançon, and Heide Götz. "How to search the indescribable - Search concepts for products requiring parametric and/or product-by-process definitions." World Patent Information 54 (September 2018): S51—S58. http://dx.doi.org/10.1016/j.wpi.2017.03.004.

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19

Kim, Dongha, JongRoul Woo, Jungwoo Shin, Jongsu Lee, and Yongdai Kim. "Can search engine data improve accuracy of demand forecasting for new products? Evidence from automotive market." Industrial Management & Data Systems 119, no. 5 (June 10, 2019): 1089–103. http://dx.doi.org/10.1108/imds-08-2018-0347.

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Purpose The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion. Design/methodology/approach This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion. Findings The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions. Research limitations/implications As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources. Originality/value This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.

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20

Gawande, Prashant. "AI Multi Agent Shopping System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 10, 2021): 424–25. http://dx.doi.org/10.22214/ijraset.2021.34934.

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Анотація:

The aim of "Ai multi agent Shopping system" helps the user to search products and compare the price of the particular product and shows the least price of the product. Nowadays e-shopping systems use the Internet as its primary medium for transactions. E-shopping has grown in popularity over the years, mainly because people find it easy to buy number of products comfortably from their places. This technology is used to improve the customer’s needs which include faster response time. Agent for e-Shopping creates connectivity on an anytime-anywhere & any basis to provide the specific products required by the customers based on optimization and scalability. When a user search for a particular product the agent search that product on the site which has been given and compare that particular products price and it will show the least price on the right side. The conclusion shows that the system can help customers to save time for Internet shopping.

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21

Tokarev, Boris. "MARKETING FOR INNOVATIVE PRODUCT DEVELOPMENT: PERFECTION SEARCH." VESTNIK UNIVERSITETA, no. 5 (2018): 42–47. http://dx.doi.org/10.26425/1816-4277-2018-5-42-47.

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22

Kuksov, Dmitri. "Buyer Search Costs and Endogenous Product Design." Marketing Science 23, no. 4 (November 2004): 490–99. http://dx.doi.org/10.1287/mksc.1040.0080.

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23

Hafernik, Carolyn T., Bin Cheng, Paul Francis, and Bernard J. Jansen. "Mapping user search queries to product categories." Proceedings of the American Society for Information Science and Technology 48, no. 1 (2011): 1–10. http://dx.doi.org/10.1002/meet.2011.14504801111.

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24

Infante, Virginia, Bruce G. Cameron, and Jakub Kwapisz. "Commonality opportunity search in industrial product portfolios." International Journal of Technology Management 81, no. 3/4 (2019): 258. http://dx.doi.org/10.1504/ijtm.2019.10027034.

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25

Kwapisz, Jakub, Virginia Infante, and Bruce G. Cameron. "Commonality opportunity search in industrial product portfolios." International Journal of Technology Management 81, no. 3/4 (2019): 258. http://dx.doi.org/10.1504/ijtm.2019.105318.

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26

Amblee, Naveen, Rahat Ullah, and Wonjoon Kim. "Do product reviews really reduce search costs?" Journal of Organizational Computing and Electronic Commerce 27, no. 3 (May 19, 2017): 199–217. http://dx.doi.org/10.1080/10919392.2017.1332142.

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27

Withnell, Robert H., and Jill Lodde. "In search of basal distortion product generators." Journal of the Acoustical Society of America 120, no. 4 (October 2006): 2116–23. http://dx.doi.org/10.1121/1.2338291.

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28

Ward, Michael R., and Michael J. Lee. "Internet shopping, consumer search and product branding." Journal of Product & Brand Management 9, no. 1 (February 2000): 6–20. http://dx.doi.org/10.1108/10610420010316302.

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29

Ursu, Raluca M., and Daria Dzyabura. "Retailers’ product location problem with consumer search." Quantitative Marketing and Economics 18, no. 2 (September 12, 2019): 125–54. http://dx.doi.org/10.1007/s11129-019-09214-6.

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30

Guan, Chong, and Shun Yin Lam. "Product Rating Statistics as Consumer Search Aids." Journal of Interactive Marketing 48 (November 2019): 51–70. http://dx.doi.org/10.1016/j.intmar.2019.02.003.

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31

Petrikaitė, Vaiva. "A search model of costly product returns." International Journal of Industrial Organization 58 (May 2018): 236–51. http://dx.doi.org/10.1016/j.ijindorg.2017.06.003.

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32

Yu, Tan, Jingjing Meng, Chen Fang, Hailin Jin, and Junsong Yuan. "Product Quantization Network for Fast Visual Search." International Journal of Computer Vision 128, no. 8-9 (April 23, 2020): 2325–43. http://dx.doi.org/10.1007/s11263-020-01326-x.

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33

Jung, Hae Won (Henny), and Ajay Subramanian. "Search, product market competition and CEO pay." Journal of Corporate Finance 69 (August 2021): 101981. http://dx.doi.org/10.1016/j.jcorpfin.2021.101981.

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34

Rakhmawati, Nur Aini, and Adnan Mauludin Fajriyadi. "Search Halal Products Using BM25F and the Analytic Hierarchy Process." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 5, no. 2 (August 6, 2019): 171. http://dx.doi.org/10.26418/jp.v5i2.33007.

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Анотація:

In Indonesia, the Institute of Food and Drug Administration (LPPOM) MUI is the official institution that provides information about halal products. However, the lack of information is provided on the website. Halal Nutrition Food is an application that has a function to facilitate the search for halal products that are incorporated in an integrated database in the form of Linked Open Data. To improve the searching features, we exploit BM25F. BM25F can process structured documents such as instances in RDF. The BM25F return the answer based on four fields: product name, manufacturer name, product ID and ingredient name. The weight of each field is calculated using the Analytic Hierarchy Process (AHP) where the product name gets the highest weight value. The number of keywords and the keywords occurrences influence the score of query results.

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35

Simms, Christopher Don, and Thomas Lager. "In search of a product innovation work process for non-assembled products." International Journal of Technological Learning, Innovation and Development 12, no. 3 (2020): 224. http://dx.doi.org/10.1504/ijtlid.2020.10034464.

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36

Lager, Thomas, and Christopher Don Simms. "In search of a product innovation work process for non-assembled products." International Journal of Technological Learning, Innovation and Development 12, no. 3 (2020): 224. http://dx.doi.org/10.1504/ijtlid.2020.112230.

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37

Chen, Peiyu, Lorin M. Hitt, Yili Hong, and Shinyi Wu. "Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application." Information Systems Research 32, no. 4 (December 2021): 1470–89. http://dx.doi.org/10.1287/isre.2021.1041.

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Анотація:

Search and experience goods, as well as vertical and horizontal differentiation, are fundamental concepts of great importance to business operations and strategy. In our paper, we propose a set of theory-grounded data-driven measures that allow us to measure not only product type (search vs. experience and horizontal vs. vertical differentiation) but also sources of uncertainty and to what extent consumer reviews help resolve uncertainty. We used product rating data from Amazon.com to illustrate the relative importance of fit in driving product utility and the importance of search for determining fit for each product category at Amazon. Our results also show that, whereas ratings based on verified purchasers are informative of objective product values, the current Amazon review system appears to have limited ability to resolve fit uncertainty. Industry practitioners could utilize our approaches to quantitatively measure product positioning to support marketing strategy for retailers and manufacturers, covering an expanded group of products.

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38

Jbene, Mourad, Smail Tigani, Saadane Rachid, and Abdellah Chehri. "Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search." Big Data and Cognitive Computing 5, no. 3 (August 13, 2021): 35. http://dx.doi.org/10.3390/bdcc5030035.

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Анотація:

In the age of information overload, customers are overwhelmed with the number of products available for sale. Search engines try to overcome this issue by filtering relevant items to the users’ queries. Traditional search engines rely on the exact match of terms in the query and product meta-data. Recently, deep learning-based approaches grabbed more attention by outperforming traditional methods in many circumstances. In this work, we involve the power of embeddings to solve the challenging task of optimizing product search engines in e-commerce. This work proposes an e-commerce product search engine based on a similarity metric that works on top of query and product embeddings. Two pre-trained word embedding models were tested, the first representing a category of models that generate fixed embeddings and a second representing a newer category of models that generate context-aware embeddings. Furthermore, a re-ranking step was performed by incorporating a list of quality indicators that reflects the utility of the product to the customer as inputs to well-known ranking methods. To prove the reliability of the approach, the Amazon reviews dataset was used for experimentation. The results demonstrated the effectiveness of context-aware embeddings in retrieving relevant products and the quality indicators in ranking high-quality products.

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39

Branje, Carmen, Alisha Yang, and Mark Chignell. "Beauty and the Beastly Search: Finding Luxury in a Product Hierarchy." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1520–24. http://dx.doi.org/10.1177/1071181320641364.

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Анотація:

There has been considerable research on design of menu hierarchies in general spanning several decades. However there is much less research on menus relating to specific types of product in online retail settings. Thus there is little guidance in the research literature on specific issues such as how to place luxury items within a beauty product hierarchy, which is the focus of this paper. We report on a study that addressed this problem for an ecommerce site associated with a large Canadian retailer. In a within subjects design, participants searched for four beauty-related products (two of which were classed as “luxury” items) either in a hierarchy where luxury items were intermingled with other products addressing the same need (the “Combined” condition), or in a hierarchy where there was a split between luxury and non-luxury products at the top level (the “Split” condition). Segregating luxury products in the product hierarchy was found to lead to significantly slower, and more lengthy (in terms of links traversed), searches. Searches were found to be more efficient in the “Combined” condition than the “Split” Condition both when searching for luxury items, and when searching for non-luxury items. This work has implications for existing brick-and-mortar retailers moving into or expanding e-commerce portals. Our results suggest that the separation of luxury from non-luxury items in bricks-and-mortar stores does not transfer well to online product hierarchies, where similar segregation leads to poorer digital navigation performance.

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40

Bohm, Matt R., Robert B. Stone, and Simon Szykman. "Enhancing Virtual Product Representations for Advanced Design Repository Systems." Journal of Computing and Information Science in Engineering 5, no. 4 (February 9, 2005): 360–72. http://dx.doi.org/10.1115/1.1884618.

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This paper describes the transformation of an existing set of heterogeneous product knowledge into a coherent design repository that supports product design knowledge archival and web-based search, display, and design model and tool generation. Guided by design theory, existing product information was analyzed and compared against desired outputs to ascertain what information management structure was needed to produce design resources pertinent to the design process. Several test products were catalogued to determine what information was essential without being redundant in representation. This set allowed for the creation of a novel single point of entry application for product information and the development of a relational database for design knowledge archival. Web services were then implemented to support design knowledge retrieval through search, browse, and real-time design tool generation. Further explored in this paper are the fundamental enabling technologies of the design repository system. Additionally, repository-generated design tools are scrutinized alongside human-generated design tools for validation. Through this process researchers have been able to improve the way in which artifact data are gathered, archived, distributed and used.

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41

Ke, T. Tony, and Song Lin. "Informational Complementarity." Management Science 66, no. 8 (August 2020): 3699–716. http://dx.doi.org/10.1287/mnsc.2019.3377.

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Many products have similar or common attributes and are thus correlated. We show that, when these attributes are uncertain for consumers, a complementarity effect can arise among competing products in the sense that the lower price of one product may increase the demands for the others. This effect occurs when consumers sequentially search for information about both common and idiosyncratic product attributes before purchase. We characterize the optimal search strategy for the correlated search problem, provide the conditions for the existence of the complementarity effect, and show that the effect is robust under a wide range of alternative assumptions. We further explore the implications of the effect for pricing. When firms compete in price, although product correlation may weaken differentiation between the firms, the complementarity effect owing to correlated search may raise equilibrium price and profit. This paper was accepted by Matthew Shum, marketing.

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42

Maier, Erik. "The Negative Effect of Product Image Inconsistency on Product Overviews During the Online Product Search." International Journal of Electronic Commerce 23, no. 1 (January 2, 2019): 110–43. http://dx.doi.org/10.1080/10864415.2018.1512281.

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43

Shin, Bong-Sup. "Images of Australia and its Product in Korean Market: In the Case of Beef." International Area Review 12, no. 2 (September 2009): 177–90. http://dx.doi.org/10.1177/223386590901200209.

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The purpose of this study was two-fold. First, tries to validate the correlation between country image and information search behavior toward its product. Secondly, investigate the effect of country image on the purchase intention toward its product. Well-developed questionnaire was distributed to 168 consumers residing in Seoul metropolitan area. Results of survey provided empirical support that country image is correlated to information search behavior, and also country image significantly affects purchase intention. That is, the higher perceived country image, the lower the customer's information search effort and higher purchase intention toward its product. The research results provide important message to Australia and for its products marketed in Korea.

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44

Sevilla, Julio, and Claudia Townsend. "The Space-to-Product Ratio Effect: How Interstitial Space Influences Product Aesthetic Appeal, Store Perceptions, and Product Preference." Journal of Marketing Research 53, no. 5 (October 2016): 665–81. http://dx.doi.org/10.1509/jmr.13.0601.

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The authors identify and examine the effect of space-to-product ratio on consumer response; very generally, consumers perceive products as more valuable when more space is devoted to their display. In both lab and field studies, the authors find that this phenomenon influences total sales, purchase likelihood, and even perceived product experience (taste perceptions). More interstitial space increases perceptions of individual products as more aesthetically pleasing and the store as more prestigious. The authors find these effects across a variety of product categories and rule out a number of competing alternative explanations that are based on perceptions of product popularity, scarcity, assortment search difficulty, and messiness.

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45

Terjesen, Siri, and Pankaj C. Patel. "In Search of Process Innovations: The Role of Search Depth, Search Breadth, and the Industry Environment." Journal of Management 43, no. 5 (March 18, 2015): 1421–46. http://dx.doi.org/10.1177/0149206315575710.

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Although a significant corpus of work focuses on the impact of search strategies on product innovations, we have a limited understanding of search strategies for process innovations, including the potential role of the industry environment. Process innovations are central to improving a firm’s productivity and contributing to efficiency and gross domestic product growth. As a result of the complexity of identifying, developing, and implementing process innovations, firms increasingly draw on external sources of knowledge. Building on key tenets in the knowledge search, innovation, and industry environment literatures, we investigate search strategies, process innovations, and industry dynamics in a sample of 505 firms spanning 23 manufacturing industries. We find that search breadth is negatively related to process innovation outcomes and that search depth is positively related to process innovation outcomes. Furthermore, high industry process heterogeneity mitigates the negative impact of search breadth on process innovation such that firms employing broad search strategies in highly process heterogeneous industries are more likely to introduce process innovations. In industries with greater productivity growth, the positive relationship between search depth and process innovation is stronger.

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46

Liu, Rui, Tianyi Wu, and Barzan Mozafari. "A Bandit Approach to Maximum Inner Product Search." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4376–83. http://dx.doi.org/10.1609/aaai.v33i01.33014376.

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There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS). To achieve fast query time, state-of-the-art techniques require significant preprocessing, which can be a burden when the number of subsequent queries is not sufficiently large to amortize the cost. Furthermore, existing methods do not have the ability to directly control the suboptimality of their approximate results with theoretical guarantees. In this paper, we propose the first approximate algorithm for MIPS that does not require any preprocessing, and allows users to control and bound the suboptimality of the results. We cast MIPS as a Best Arm Identification problem, and introduce a new bandit setting that can fully exploit the special structure of MIPS. Our approach outperforms state-of-the-art methods on both synthetic and real-world datasets.

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47

Vandic, Damir, Steven Aanen, Flavius Frasincar, and Uzay Kaymak. "Dynamic Facet Ordering for Faceted Product Search Engines." IEEE Transactions on Knowledge and Data Engineering 29, no. 5 (May 1, 2017): 1004–16. http://dx.doi.org/10.1109/tkde.2017.2652461.

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48

George, Carole A. "Lessons learned: usability testing a federated search product." Electronic Library 26, no. 1 (February 15, 2008): 5–20. http://dx.doi.org/10.1108/02640470810851707.

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49

Chiang, I. Robert, and Manuel A. Nunez. "Improving Web-Catalog Design for Easy Product Search." INFORMS Journal on Computing 19, no. 4 (November 2007): 510–19. http://dx.doi.org/10.1287/ijoc.1060.0184.

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50

Coley, D. A., and D. Winters. "Genetic algorithm search efficacy in aesthetic product spaces." Complexity 3, no. 2 (November 1997): 23–27. http://dx.doi.org/10.1002/(sici)1099-0526(199711/12)3:2<23::aid-cplx5>3.0.co;2-n.

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