Moshe Unger

• Doctor of Philosophy
• Assistant Professor at Tel Aviv University - Coller School of Management at Tel Aviv University

Contextual situations, such as having dinner at a restaurant on Friday with the spouse, became a useful mechanism to represent context in context-aware recommender systems (CARS). Prior research has shown important advantages of using latent embedding representation approaches to model contextual information in the Euclidean space leading to better...

We propose the use of a deep learning architecture, called RETINA, to predict multi-alternative, multi-attribute consumer choice from eye movement data. RETINA directly uses the complete time series of raw eye-tracking data from both eyes as input to state-of-the art Transformer and Metric Learning Deep Learning methods. Using the raw data input el...

Although building a 360-degree comprehensive view of a customer has been a long-standing goal in marketing, this challenge has not been successfully addressed in many marketing applications because fractured customer data stored across different “silos” are hard to integrate under “one roof” for several reasons. Instead of integrating customer data...

In this paper, we propose hierarchical representation of latent contextual information that captures contextual situations in which users are recommended particular items. We also introduce an algorithm that converts unstructured latent contextual information into structured hierarchical representations. In addition, we present two general context-...

In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature space in multiple applications and services, we suggest to model contextual information in vario...

We present the Network Traffic Generator (NTG), a framework for perturbing recorded network traffic with the purpose of generating diverse but realistic background traffic for network simulation and what-if analysis in enterprise environments. The framework preserves many characteristics of the original traffic recorded in an enterprise, as well as...

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused context-aware recommender systems (CARS), man...

Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may increases both the dimensionality and sparsity of the model. Recent research has shown that modeling contextual information as a la...

Context-aware systems enable the sensing and analysis of user context in order to provide personalised services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilised to infer users’ dynamic preferences that are learned over time. We suggest novel methods for inferring the ca...

Context-aware systems enable the sensing and analysis of user context in order to provide personalized services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilized to infer users' dynamic preferences. We present a novel method for inferring contextual user preferences by u...

Attack propagation models within honeypot systems aim at providing insights about attack strategies that target multiple honeypots, rather than analyzing attacks on each honeypot separately. Traditional attack propagation models focus on building a single probabilistic model. This modeling approach may be misleading, since it does not take into con...

The emergence and penetration of smart mobile devices has given rise to the development of context-aware systems that utilize sensors to collect available data about users in order to improve various user services. Recently, the use of context-aware recommender systems (CARS) aimed at recommending items to users has expanded, particularly those tha...

Context-aware computing aims at tailoring services to the user's circumstances and surroundings. Our study examines how data collected from mobile devices can be utilized to infer users' behavior and environment. We present the results and the lessons learned from a two-week user study of 40 students. The data collection was performed using Context...