Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"Product recommendation"”.
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Data-driven methods for personalized product recommendation systems
… development of increasingly more sophisticated product recommendation systems. Thus, a competitive edge in this rapidly growing sector could be worth up to millions of dollars in revenue for an online seller. Motivated by this increasingly prevalent problem, we propose an innovative model that …
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ELECTRONIC WORD-OF-MOUTH: APPLICATIONS IN PRODUCT RECOMMENDATION AND CRISIS INFORMATION DISSEMINATION
… (eWOM) plays a central role starting from product recommendations to social awareness, which is the quintessence of this thesis. It contains three essays. The first one aims to study how eWOM, in the form of user comments, is beneficial in recommendations of high-scale products like mobile …
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Insurance recommendation engine using a combined collaborative filtering and neural network approach
A recommendation engine for insurance modelling was designed, implemented and tested using a neural network and collaborative filtering approach. The recommendation engine aims to suggest suitable insurance products for new or existing customers, based on their features or selection history. The …
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Personalized government online services with recommendation techniques
… emerge. However, existing personalization with recommendation techniques focuses on text document retrieval and e-commerce product recommendation domain. Personalization and recommendation applications in e-government have paid relatively little research attention. Many mechanisms have been …
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Structures & algorithms in hyperbolic random graphs
… as a proof of concept, we embed the Amazon product recommendation network and observe that products from the same category are mapped close together.
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Aspect-based sentiment analysis for social recommender systems.
… content, experiences and interactions to provide recommendations to users. The retrieval and ranking of products, using similarity knowledge, is central to the recommendation architecture. To enhance recommendation performance, having an effective representation of products is essential. Social …
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Active evaluation of predictive models
… handwritten digit recognition, and personalized product recommendation. In general, they are used to predict a target label for a given data instance. In order to make an informed decision about the deployment of a predictive model, it is crucial to know the model’s approximate performance. To …
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Uji viabilitas dan virulensi nematoda entomopatogen (Steinernema spp.) terhadap bahan aktif insektisida golongan Insect Growth Regulator (IGR)
… active concentration material buprofezine 0.2% (product recommendation), 0.1% (low concentration) and siromazine 0.06% (product recommandation), 0.1% (low recommandation) and siromazine 0.06% (product recommandation), siromazine 0.03% (low concentration). The data was analyzed by vary-variate …
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Statistical Learning Approaches to Information Filtering
… has become crucial in many applications, like product recommendation, image retrieval, spam email filtering, news filtering, and web navigation etc.. An information filtering system must be able to understand users' information needs. Existing approaches either infer a user's profile by …
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Choice modeling and recommendation optimization in presence of context effects
… these effects in revenue management systems and product recommendation modules. In this thesis, we propose three sets of machine learning models in order to capture these effects in different settings with different input data structures. For these settings, we also study combinatorial problems …
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Domain-based Frameworks and Embeddings for Dynamics over Networks
… 95% of outbreaks. Similarly, our framework for product recommendation in e-commerce for queries with sparse engagement data resulted in a 34% improvement over the current Walmart.com search engine. Similarly, our novel framework leads to a near optimal algorithms, with additive approximation …