2 citations · 5 across the 4 of their papers we have counts for
6 papers
Fine-grained large-scale content recommendations for MSX sellers
Manpreet Singh, Ravdeep Pasricha, Ravi Prasad Kondapalli +6
One of the most critical tasks of Microsoft sellers is to meticulously track and nurture potential business opportunities through proactive engagement and tailored solutions. Recom…
A case study of Generative AI in MSX Sales Copilot: Improving seller productivity with a real-time question-answering system for content recommendation
Manpreet Singh, Ravdeep Pasricha, Nitish Singh +4
In this paper, we design a real-time question-answering system specifically targeted for helping sellers get relevant material/documentation they can share live with their customer…
Adaptive Granularity in Tensors: A Quest for Interpretable Structure
Ravdeep Pasricha, Ekta Gujral, Evangelos E. Papalexakis
Data collected at very frequent intervals is usually extremely sparse and has no structure that is exploitable by modern tensor decomposition algorithms. Thus the utility of such t…
OCTen: Online Compression-based Tensor Decomposition
Ekta Gujral, Ravdeep Pasricha, Tianxiong Yang +1
Tensor decompositions are powerful tools for large data analytics as they jointly model multiple aspects of data into one framework and enable the discovery of the latent structure…
Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition
Ravdeep Pasricha, Ekta Gujral, Evangelos E. Papalexakis
Tensor decompositions are used in various data mining applications from social network to medical applications and are extremely useful in discovering latent structures or concepts…
SamBaTen: Sampling-based Batch Incremental Tensor Decomposition
Ekta Gujral, Ravdeep Pasricha, Evangelos E. Papalexakis
Tensor decompositions are invaluable tools in analyzing multimodal datasets. In many real-world scenarios, such datasets are far from being static, to the contrary they tend to gro…