publications

2026

1. Two-Tier Visual Triggering for Adaptive Shopping Recommendations at Pinterest

   Kshiteesh Hegde, Ai Zhang, Kayoung Lee, and 3 more authors

   In Proceedings of the OARS Workshop at ACM RecSys 2026, 2026

2025

1. Scaling Taxonomy-Free Image Labeling with Generative AI: A Multimodal Search Application

   Zhenjie Zhang, Sainan Chen, Kshiteesh Hegde, and 4 more authors

   In Proceedings of the 4th Workshop on End-to-End Customer Journey Optimization at the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025
2. Introducing Multi-Modal Search at Pinterest: A New User Experience

   Mengze Irene Li, Pouya Rezazadeh Kalehbasti, Sainan Chen, and 5 more authors

   In Proceedings of the 4th Workshop on End-to-End Customer Journey Optimization at the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025

2024

1. Cause and effect: Can large language models truly understand causality?

   Swagata Ashwani, Kshiteesh Hegde, Nishith Reddy Mannuru, and 6 more authors

   In Proceedings of the AAAI Symposium Series, 2024

2019

1. Network Lens: Node Classification in Topologically Heterogeneous Networks

   Kshiteesh Hegde, and Malik Magdon-Ismail

   arXiv preprint arXiv:1901.09681, 2019
2. The intrinsic scale of networks is small

   Malik Magdon-Ismail, and Kshiteesh Hegde

   In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2019

2018

1. Separating Terrorist-Like Topological Signatures Embedded in Benign Networks

   Kshiteesh Hegde, and Malik Magdon-Ismail

   In MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM), 2018
2. Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification

   Kshiteesh Hegde, Malik Magdon-Ismail, Ram Ramanathan, and 1 more author

   2018

2017

1. Clustering, Prominence and Social Network Analysis on Incomplete Networks

   Kshiteesh Hegde, Malik Magdon-Ismail, Boleslaw Szymanski, and 1 more author

   In Complex Networks & Their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016), 2017

2016

1. Recommendations For Streaming Data

   Karthik Subbian, Charu Aggarwal, and Kshiteesh Hegde

   In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016

   Abs

   Recommender systems have become increasingly popular in recent years because of the broader popularity of many web-enabled electronic commerce applications. However, most recommender systems today are designed in the context of an offline setting. The online setting is, however, much more challenging because the existing methods do not work very effectively for very large-scale systems. In many applications, it is desirable to provide real-time recommendations in large-scale scenarios. The main problem in applying streaming algorithms for recommendations is that the in-core storage space for memory-resident operations is quite limited. In this paper, we present a probabilistic neighborhood-based algorithm for performing recommendations in real-time. We present experimental results, which show the effectiveness of our approach in comparison to state-of-the-art methods.