Готові списки джерел за темами / Product search

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Добірка наукової літератури з теми "Product search"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Product search"

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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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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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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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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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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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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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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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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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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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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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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Дисертації з теми "Product search"

Pereira, Juliana Alves. "Search-based product configuration in software product lines." Universidade Federal de Minas Gerais, 2014. http://hdl.handle.net/1843/ESBF-9Q4FQ9.

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Software product lines (SPL) is a software engineering method for creating configurable software systems that can be adapted to a variety of requirements. SPL uses feature models to document the commonalities and variabilities of products. The key challenge is to derive a product configuration that satisfies all business and costumer requirements. Customer requirements are usually neglected, although product configuration has already been investigated in the literature. This dissertation presents a search-based approach to tackle the problem of finding the optimal product configuration that maximizes the customer satisfaction. The latter is modeled as an optimization problem. An exact algorithm and a heuristic are proposed to solve the problem and implemented in a prototype tool. Computational experiments showed that the exact algorithm can find the optimal product configuration for real-life instances found in the literature and that the optimality gap of the heuristic is at most 3%.
Linha de produtos de software (LPS) é um método de engenharia de software para a criação de sistemas de software configuráveis que podem ser adaptados a uma variedade de conjuntos de requisitos. LPS comumente usa modelo de características para capturar e documentar as semelhanças e variabilidades de produtos. O principal desafio é derivar uma configuração de produto que satisfaça todos os requisitos do negócio e do cliente. Os requisitos dos clientes são normalmente negligenciados, embora a configuração do produto já foi investigada na literatura. Esta dissertação apresenta uma abordagem de engenharia de software baseada em busca para resolver o problema de encontrar a configuração de produto ótima que maximiza a satisfação do cliente. Este último é modelado como um problema de otimização. Um algoritmo exato e uma heurística são propostas para resolver o problema e implementado em uma ferramenta protótipo. Experimentos computacionais mostraram que o algoritmo exato pode encontrar a configuração ótima do produto para instancias reais da literatura e que a diferença entre o resultado heurístico e a solução ótima é de no máximo 3%.

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Katila, Riitta. "In search of innovation : search determinants of new product introductions /." Digital version:, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p9992831.

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Walther, Maximilian Thilo. "Federated Product Information Search and Semantic Product Comparisons on the Web." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-74759.

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Анотація:

Product information search has become one of the most important application areas of the Web. Especially considering pricey technical products, consumers tend to carry out intensive research activities previous to the actual acquisition for creating an all-embracing view on the product of interest. Federated search backed by ontology-based product information representation shows great promise for easing this research process. The topic of this thesis is to develop a comprehensive technique for locating, extracting, and integrating information of arbitrary technical products in a widely unsupervised manner. The resulting homogeneous information sets allow a potential consumer to effectively compare technical products based on an appropriate federated product information system
Die Produktinformationssuche hat sich zu einem der bedeutendsten Themen im Web entwickelt. Speziell im Bereich kostenintensiver technischer Produkte führen potenzielle Konsumenten vor dem eigentlichen Kauf des Produkts langwierige Recherchen durch um einen umfassenden Überblick für das Produkt von Interesse zu erlangen. Die föderierte Suche in Kombination mit ontologiebasierter Produktinformationsrepräsentation stellt eine mögliche Lösung dieser Problemstellung dar. Diese Dissertation stellt Techniken vor, die das automatische Lokalisieren, Extrahieren und Integrieren von Informationen für beliebige technische Produkte ermöglichen. Die resultierenden homogenen Produktinformationen erlauben einem potenziellen Konsumenten, zugehörige Produkte effektiv über ein föderiertes Produktinformationssystem zu vergleichen

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Zeng, Kaiman. "Next Generation of Product Search and Discovery." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2312.

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Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics, and CD/DVDs, online shopping has surpassed or even replaced the traditional shopping method. Compared with traditional retailing, e-commerce is information intensive. One of the key factors to succeed in e-business is how to facilitate the consumers’ approaches to discover a product. Conventionally a product search engine based on a keyword search or category browser is provided to help users find the product information they need. The general goal of a product search system is to enable users to quickly locate information of interest and to minimize users’ efforts in search and navigation. In this process human factors play a significant role. Finding product information could be a tricky task and may require an intelligent use of search engines, and a non-trivial navigation of multilayer categories. Searching for useful product information can be frustrating for many users, especially those inexperienced users. This dissertation focuses on developing a new visual product search system that effectively extracts the properties of unstructured products, and presents the possible items of attraction to users so that the users can quickly locate the ones they would be most likely interested in. We designed and developed a feature extraction algorithm that retains product color and local pattern features, and the experimental evaluation on the benchmark dataset demonstrated that it is robust against common geometric and photometric visual distortions. Besides, instead of ignoring product text information, we investigated and developed a ranking model learned via a unified probabilistic hypergraph that is capable of capturing correlations among product visual content and textual content. Moreover, we proposed and designed a fuzzy hierarchical co-clustering algorithm for the collaborative filtering product recommendation. Via this method, users can be automatically grouped into different interest communities based on their behaviors. Then, a customized recommendation can be performed according to these implicitly detected relations. In summary, the developed search system performs much better in a visual unstructured product search when compared with state-of-art approaches. With the comprehensive ranking scheme and the collaborative filtering recommendation module, the user’s overhead in locating the information of value is reduced, and the user’s experience of seeking for useful product information is optimized.

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Cherednichenko, Olga, Maryna Anatoliivna Vovk, Olga Kanishcheva, and Mikhail Godlevskyi. "Towards Improving the Search Quality on the Trading Platforms." Thesis, Springer International Publishing AG, 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/46681.

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In this paper, the problem of the search quality on the trading platforms, such AliExpress, eBay and others is explored, the major types of problems that arise in product search by customers are considered. The usage of the classical clusterization algorithms for grouping similar products according to their descriptions is studied. A data set for experimenting consists of different items (smartphones) from e-shop eBay is developed. Each entity in this corpus photos and a product description are given. These texts are used for item comparing in order to perform similar groups or similar items. The results show that the k-means algorithm is good for preliminary grouping but for detailed processing, other methods and approaches are required.

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Sauer, Paul Lawrence. "Videotex (modality) search condition and product familiarity effects on comprehension of product messages and attitudes toward the product." Connect to resource, 1985. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262874708.

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Zhang, Jiyong. "Design and evaluation issues for user-centric online product search." Lausanne : École polytechnique fédérale de Lausanne, 2008. http://aleph.unisg.ch/volltext/B461591_Zhang_Design_and_evaluation_issues.pdf.

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Mahdi, Surya. "Search strategy in product innovation : theory and evidence from agrochemicals." Thesis, University of Sussex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270717.

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Jian, Guo. "Knowledge search for new product development : a multi-agent based methodology." Thesis, University of Greenwich, 2011. http://gala.gre.ac.uk/8786/.

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Manufacturers are the leaders in developing new products to drive productivity. Higher productivity means more products based on the same materials, energy, labour, and capitals. New product development plays a critical role in the success of manufacturing firms. Activities in the product development process are dependent on the knowledge of new product development team members. Increasingly, many enterprises consider effective knowledge search to be a source of competitive advantage. This research presents an exploratory case study conducted at an aircraft manufacturer. This investigation uncovered six, empirically derived and theoretically informed, problems to enterprise knowledge search. They have been articulated as (i) the effectual web bandwidth limits search speed; (ii) less relevant search results based on word-frequency recognition models of search engine; (iii) un-useable techniques for enterprise search; (iv) rigour security, reliability, and company policy; (v) poor search performance about unstructured enterprise knowledge; (vi) the lack of tacit knowledge sharing. Existing search methodologies have focused on the internet search, rather than providing effective search for enterprise. This research aim is developed to assist the manufacturing enterprise in meeting the industrial requirements in the following way: a methodology and system that can improve the information and knowledge search performance in new product development process. Based on the exploratory case findings, a knowledge search methodology and system has been developed. Agent technology is used to fulfil the requirements of enterprise search. Some initial tests were conducted to better understand implementation issues and future deployment of the methodology and system in practice.

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Dennis, Aaron W. "Algorithms for Learning the Structure of Monotone and Nonmonotone Sum-Product Networks." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6188.

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The sum-product network (SPN) is a recently-proposed generative, probabilistic model that is guaranteed to compute any joint or any marginal probability in time linear in the size of the model. An SPN is represented as a directed, acyclic graph (DAG) of sum and product nodes, with univariate probability distributions at the leaves. It is important to learn the structure of this DAG since the set of distributions representable by an SPN is constrained by it. We present the first batch structure learning algorithm for SPNs and show its advantage over learning the parameters of an SPN with fixed architecture. We propose a search algorithm for learning the structure of an SPN and show that its ability to learn a DAG-structured SPN makes it better for some tasks than algorithms that only learn tree-structured SPNs. We adapt the structure search algorithm to learn the structure of an SPN in the online setting and show that two other methods for online SPN structure learning are slower or learn models with lower likelihood. We also propose to combine SPNs with an autoencoder to model image data; this application of SPN structure learning shows that both models benefit from being combined.We are also the first to propose a distinction between nonmonotone and monotone SPNs, or SPNs with negative edge-weights and those without, respectively. We prove several important properties of nonmonotone SPNs, propose algorithms for learning a special class of nonmonotone SPN called the twin SPNs, and show that allowing negative edge-weights can help twin SPNs model some distributions more compactly than monotone SPNs.

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Книги з теми "Product search"

Fatema, Yousuf Anisatul, and Centre for Policy Dialogue (Bangladesh), eds. In search of new products and new markets for Bangladesh. Dhaka: Centre for Policy Dialogue, 2004.

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Simone, Alejandro. In search of coincident and leading indicators of economic activity in Argentina. [Washington, D.C.]: International Monetary Fund, Western Hemisphere Department, 2001.

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Hortaçsu, Ali. Product differentiation, search costs, and competition in the mutual fund industry: A case study of S&P 500 index funds. Cambridge, Mass: National Bureau of Economic Research, 2003.

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Akulov, Nikolay. TESTING OF SEDIMENTARY DEPOSITS AT DIAMOND SEARCHING WORKS. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_62021f624d9c18.71126120.

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The book contains materials on the search for modern and buried alluvial and primary deposits of diamonds. Much attention is paid to prospecting testing of potentially diamondiferous deposits and provides information on all types of diamondiferous rocks currently known. It is addressed primarily to young geologists who have embarked on a search for diamond deposits. It will find the answer to many questions by many geologists, prospectors and prospectors, leading the search for gold and diamonds. While this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

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Products liability and the search for justice. Durham, N.C: Carolina Academic Press, 1993.

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Trout, Jack. In search of the obvious: The antidote for today's marketing mess. Hoboken, N.J: John Wiley & Sons, Inc., 2008.

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The invisible grail: In search of the true language of brands. London: Texere, 2003.

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Cooper, Andrew Fenton. In between countries: Australia, Canada, and the search for order in agricultural trade. Montreal: McGill-Queee's University Press, 1997.

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Shechambo, Fanuel C. The search for transaction mechanisms for agricultural products in Tanzania: The case of Lushoto District. Berlin: Verlag Köster, 1993.

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Nwokedi, Joe. Search for oil market stability: Dr. Lukman in OPEC. Ikeja Lagos: Pathway Communications, 1989.

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Частини книг з теми "Product search"

McGaw, Jim. "Implementing Product Search." In Beginning Django E-Commerce, 179–92. Berkeley, CA: Apress, 2009. http://dx.doi.org/10.1007/978-1-4302-2536-2_8.

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Nielsen, H. J. "Systematic Search for Product Ideas." In Creativity and Innovation: towards a European Network, 111–26. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-2827-5_20.

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Vandic, Damir, and Viorel Milea. "Semantic Web-Based Product Search." In Lecture Notes in Computer Science, 150–59. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14139-8_17.

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Féderle, Édipo Luis, Thiago do Nascimento Ferreira, Thelma Elita Colanzi, and Silvia Regina Vergilio. "Optimizing Software Product Line Architectures with OPLA-Tool." In Search-Based Software Engineering, 325–31. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22183-0_30.

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Sanghikian, Nathalie, Rafael Martinelli, and Victor Abu-Marrul. "A Hybrid VNS for the Multi-product Maritime Inventory Routing Problem." In Variable Neighborhood Search, 111–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69625-2_9.

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Wahdan, Hayam G., Sally S. Kassem, and Hisham M. E. Abdelsalam. "Product Modularization Using Cuckoo Search Algorithm." In Operations Research and Enterprise Systems, 20–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53982-9_2.

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Henard, Christopher, Mike Papadakis, and Yves Le Traon. "Mutation-Based Generation of Software Product Line Test Configurations." In Search-Based Software Engineering, 92–106. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09940-8_7.

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Moskowitz, David, Howard R. Moskowitz, and Andrea Maier. "CATEGORY APPRAISAL AND INGREDIENT SEARCH." In Accelerating New Food Product Design and Development, 259–97. Chichester, UK: John Wiley & Sons Ltd and the Institute of Food Technologists, 2017. http://dx.doi.org/10.1002/9781119149330.ch17.

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Henriksen, Helle Zinner. "In Search of an Efficient EDIcebreaker." In Diffusing Software Product and Process Innovations, 277–91. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-0-387-35404-0_17.

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Stanley, Theban, Nihar Vanjara, Yanxin Pan, Ekaterina Pirogova, Swagata Chakraborty, and Abon Chaudhuri. "SIR: Similar Image Retrieval for Product Search in E-Commerce." In Similarity Search and Applications, 338–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60936-8_26.

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Тези доповідей конференцій з теми "Product search"

Nigam, Priyanka, Yiwei Song, Vijai Mohan, Vihan Lakshman, Weitian (Allen) Ding, Ankit Shingavi, Choon Hui Teo, Hao Gu, and Bing Yin. "Semantic Product Search." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330759.

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Wang, Chu, Lei Tang, Shujun Bian, Da Zhang, Zuohua Zhang, and Yongning Wu. "Reference Product Search." In WWW '19: The Web Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3308560.3316598.

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Chen, Xiangru, Haofen Wang, Xinruo Sun, Junfeng Pan, and Yong Yu. "Diversifying product search results." In the 34th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2009916.2010065.

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Das Sarma, Atish, Nish Parikh, and Neel Sundaresan. "E-commerce product search." In the 23rd International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2567948.2577272.

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Jing, Yushi, and Shumeet Baluja. "Pagerank for product image search." In Proceeding of the 17th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1367497.1367540.

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Lee, Jongwuk, Seung-won Hwang, Zaiqing Nie, and Ji-Rong Wen. "Navigation system for product search." In 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icde.2010.5447780.

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Huang, Shen, Dan Shen, Wei Feng, Catherine Baudin, and Yongzheng Zhang. "Improving product review search experiences on general search engines." In the 11th International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1593254.1593269.

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Yao, Jiawei, Jiajun Yao, Rui Yang, and Zhenyu Chen. "Product Recommendation Based on Search Keywords." In 2012 9th Web Information Systems and Applications Conference (WISA). IEEE, 2012. http://dx.doi.org/10.1109/wisa.2012.33.

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Zhang, Guixuan, Shuwu Zhang, Zhi Zeng, Hu Guan, and Xiaoqian Li. "Product image search with regional evidences." In 2016 International Conference on Progress in Informatics and Computing (PIC). IEEE, 2016. http://dx.doi.org/10.1109/pic.2016.7949527.

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Li, Beibei, Panagiotis G. Ipeirotis, and Anindya Ghose. "Improving product search with economic theory." In 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010). IEEE, 2010. http://dx.doi.org/10.1109/icdew.2010.5452727.

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Звіти організацій з теми "Product search"

Aldrich, Susan. WebSideStory Search Product Search Solution. Boston, MA: Patricia Seybold Group, July 2005. http://dx.doi.org/10.1571/pr7-14-05cc.

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Aldrich, Susan. Product Search Research Collection. Boston, MA: Patricia Seybold Group, March 2006. http://dx.doi.org/10.1571/searchcoll06.

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Aldrich, Susan. Ecommerce Search Product Comparison. Boston, MA: Patricia Seybold Group, September 2009. http://dx.doi.org/10.1571/ca09-10-09cc.

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Aldrich, Susan. Evaluating Product Search Solutions. Boston, MA: Patricia Seybold Group, May 2004. http://dx.doi.org/10.1571/ca5-27-04cc.

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Aldrich, Susan. ATG Commerce Search V.2006.1 Product Search Solution. Boston, MA: Patricia Seybold Group, December 2005. http://dx.doi.org/10.1571/pr12-22-05cc.

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Aldrich, Susan. Product Search Solution RFP Template. Boston, MA: Patricia Seybold Group, March 2004. http://dx.doi.org/10.1571/ii3-18-04cc.

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Aldrich, Susan. Search Product and Company Update. Boston, MA: Patricia Seybold Group, March 2006. http://dx.doi.org/10.1571/ta3-16-06cc.

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Aldrich, Susan. Search Product and Company Update. Boston, MA: Patricia Seybold Group, March 2006. http://dx.doi.org/10.1571/ta3-30-06cc.

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Aldrich, Susan. Search Product and Company Update. Boston, MA: Patricia Seybold Group, August 2006. http://dx.doi.org/10.1571/ta8-17-06cc.

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Aldrich, Susan. Search Product and Company Update. Boston, MA: Patricia Seybold Group, August 2006. http://dx.doi.org/10.1571/ta8-24-06cc.

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