activity
20172026
most citedCross-Market Product Recommendation

48 citations · 99 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

CoMem: Context Management with A Decoupled Long-Context Model

Yuwei Zhang, Chengyu Dong, Shuowei Jin +11

Context management enables agentic models to solve long-horizon tasks through iterative summarization of previous interaction histories. However, this process typically incurs subs…

cs.LG2021★ 1 cited

On-the-Fly Ensemble Pruning in Evolving Data Streams

Sanem Elbasi, Alican Büyükçakır, Hamed Bonab +1

Ensemble pruning is the process of selecting a subset of componentclassifiers from an ensemble which performs at least as well as theoriginal ensemble while reducing storage and co…

cs.IR2021★ 48 cited

Cross-Market Product Recommendation

Hamed Bonab, Mohammad Aliannejadi, Ali Vardasbi +2

We study the problem of recommending relevant products to users in relatively resource-scarce markets by leveraging data from similar, richer in resource auxiliary markets. We hypo…

cs.IR2019

A Multi-Task Architecture on Relevance-based Neural Query Translation

Sheikh Muhammad Sarwar, Hamed Bonab, James Allan

We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation. The translati…

cs.LG2018★ 34 cited

A Novel Online Stacked Ensemble for Multi-Label Stream Classification

Alican Büyükçakır, Hamed Bonab, Fazli Can

As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a su…

cs.LG2017★ 1 cited

GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams

Hamed R. Bonab, Fazli Can

Designing adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an on…

cs.LG2017★ 15 cited

Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers

Hamed Bonab, Fazli Can

The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble…