91 citations · 195 across the 15 of their papers we have counts for
17 papers
STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements
Moshe Unger, Alexander Tuzhilin, Michel Wedel
The present work proposes a Deep Learning architecture for the prediction of various consumer choice behaviors from time series of raw gaze or eye fixations on images of the decisi…
The Long Tail of Context: Does it Exist and Matter?
Konstantin Bauman, Alexey Vasilev, Alexander Tuzhilin
Context has been an important topic in recommender systems over the past two decades. A standard representational approach to context assumes that contextual variables and their st…
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Pan Li, Maofei Que, Zhichao Jiang +2
Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied. T…
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
Pan Li, Zhichao Jiang, Maofei Que +2
Cross domain recommender system constitutes a powerful method to tackle the cold-start and sparsity problem by aggregating and transferring user preferences across multiple categor…
Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations
Pan Li, Alexander Tuzhilin
Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. However, existing cross-domain models typica…
Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments
Panagiotis Adamopoulos, Anindya Ghose, Alexander Tuzhilin
In this paper, we examine the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers' utility and demand levels for individual produ…