Search Results - "Tuzhilin, Alexander"

Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations by Li, Pan, Tuzhilin, Alexander

“…Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. However, existing…”
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On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities by Padmanabhan, Balaji, Tuzhilin, Alexander

“…Previous work on the solution to analytical electronic customer relationship management (eCRM) problems has used either data–mining (DM) or optimization…”
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Recommending Remedial Learning Materials to Students by Filling Their Knowledge Gaps by Bauman, Konstantin, Tuzhilin, Alexander

“…We study the problem of providing recommendations to students that help them in their studies. To address this problem, we present an approach of providing…”
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Hierarchical Latent Context Representation for Context-Aware Recommendations by Unger, Moshe, Tuzhilin, Alexander

“…In this paper, we propose a hierarchical representation of latent contextual information that captures contextual situations in which users are recommended…”
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Recommendation strategies in personalization applications by Gorgoglione, Michele, Panniello, Umberto, Tuzhilin, Alexander

“…While the initial goal of recommender systems (RSes) was to reduce the information overload for Internet users and make the information retrieval more…”
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Route Recommendations for Intelligent Transportation Services by Ge, Yong, Li, Huayu, Tuzhilin, Alexander

“…The accumulated large amount of mobility data and the ability to track moving people or objects have enabled us to develop advanced mobile recommendations,…”
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Predicting consumer choice from raw eye-movement data using the RETINA deep learning architecture by Unger, Moshe, Wedel, Michel, Tuzhilin, Alexander

“…We propose the use of a deep learning architecture, called RETINA, to predict multi-alternative, multi-attribute consumer choice from eye movement data. RETINA…”
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Learning Latent Multi-Criteria Ratings from User Reviews for Recommendations by Li, Pan, Tuzhilin, Alexander

“…Multi-criteria recommender systems have been increasingly useful for helping consumers identify the most relevant items based on different dimensions of user…”
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In CARSs we trust: how context-aware recommendations affect customers' trust and other business performance measures of recommender systems by Panniello, Umberto, Gorgoglione, Michele, Tuzhilin, Alexander

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Hierarchical Contextual Embeddings for Context-Aware Recommendations (Extended Abstract) by Unger, Moshe, Tuzhilin, Alexander

“…Recommender systems (RSs) have become one of the major applications that aim to tailor items to the user's preferences. Traditional recommendation algorithms…”
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Customer relationship management and Web mining: the next frontier by Tuzhilin, Alexander

“…After a decade of successful development of new Web mining technologies, it is a good time to examine novel promising areas that will advance Web mining over…”
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Comparing context-aware recommender systems in terms of accuracy and diversity by Panniello, Umberto, Tuzhilin, Alexander, Gorgoglione, Michele

“…Although the area of context-aware recommender systems (CARS) has made a significant progress over the last several years, the problem of comparing various…”
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Dual Contrastive Learning for Efficient Static Feature Representation in Sequential Recommendations by Li, Pan, Que, Maofei, Tuzhilin, Alexander

“…Static user and item features constitute important information to be taken into account in the recommendation process. However, as these features are usually…”
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Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions by Adomavicius, G., Tuzhilin, A.

“…This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified…”
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REQUEST: A Query Language for Customizing Recommendations by Adomavicius, Gediminas, Tuzhilin, Alexander, Zheng, Rong

“…Initially popularized by Amazon.com, recommendation technologies have become widespread over the past several years. However, the types of recommendations…”
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Research Note—In CARSs We Trust: How Context-Aware Recommendations Affect Customers’ Trust and Other Business Performance Measures of Recommender Systems by Panniello, Umberto, Gorgoglione, Michele, Tuzhilin, Alexander

“…Most of the work on context-aware recommender systems has focused on demonstrating that the contextual information leads to more accurate recommendations…”
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Research note: in CARSs we trust: how context-aware recommendations affect customers' trust and other business performance measures of recommender systems by Panniello, Umberto, Gorgoglione, Michele, Tuzhilin, Alexander

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Using Context to Improve Predictive Modeling of Customers in Personalization Applications by Palmisano, C., Tuzhilin, A., Gorgoglione, M.

“…The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study…”
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Social and Economic Computing by Mao, Wenji, Tuzhilin, Alexander, Gratch, Jonathan

“…Social and economic computing is a cross-disciplinary field focusing on the development of computing technologies that consider social and economic contexts…”
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Deep Pareto Reinforcement Learning for Multi-Objective Recommender Systems by Li, Pan, Tuzhilin, Alexander

“…Optimizing multiple objectives simultaneously is an important task for recommendation platforms to improve their performance. However, this task is…”
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