Author name cluster

Abhishek Tripathi

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Enabling User Agency in Scalable Content Recommendations with Large Language Models

Yucheng Li
Gerrit J. J. Van den Burg
Wei Liu
Zhunxuan Wang
Abhishek Tripathi
Murat Sensoy

Existing content recommender systems usually depend on centrally stored interaction histories, creating vendor lock-in and disadvantagingnewerproviderswholacksufficientuserdata. Theyalsolimit users’ ability to understand, control, or edit how their preferences are represented, since profiles are learned as opaque latent vectors within provider-controlled models. We propose a user-centric alternative in which personal agents construct interpretable, editable preference profiles in natural language. Each profile item is associated with a learnable weight indicating its importance, and profiles are learned locally under full user control, laying the groundwork for high-quality personalization across multiple content providers. Recommendations are generated by matching contentwithweightedprofileembeddingsinasharedembeddingspace that is fine-tuned once using only content data and subsequently used by both content providers and personal agents. This design shifts profile ownership to users while maintaining the efficiency of existing recommender systems, as online recommendation reduces to approximate nearest-neighbor search. It further lowers the barrier for new providers, who only need to embed their content into the shared space — personalization naturally emerges from user-side profile embeddings optimized by personal agents to retrieve the most relevant content. Experiments on the MIND and Goodreads datasets show that our system outperforms strong baselines while providing transparency and editability — reimagining personalization as a process owned and controlled by the user.

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AAMAS Conference 2026 Conference Paper

LLMEvalRec: An Agentic Framework for Simulating Users to Evaluate News Recommendation Systems

Yao Ma
Abhishek Tripathi
Samuel Louvan
Wei Liu
Murat Sensoy

Evaluating news recommendation systems (NRS) presents unique challenges due to their dynamic and interactive nature coupled with evolving user interests. In the early stages of development, when user bases and historical data are scarce, it is difficult to conduct meaningful offline and online evaluations. This cold-start evaluation challenge hinders data-driven decision-making for product development and deployment. To address this, we propose LLMEvalRec, a framework that leverages Large Language Model (LLM) agents to simulate user behavior for NRS evaluation. Our approach features generative agents that automatically generate user profiles from a small number of user reading histories and perform realistic actions, while introducing the Guided Episodic Search (GUES) algorithm, which guides the automated prompt optimization process by exploring human prompt engineering practices. Experiments demonstrate that LLMEvalRec-generated data achieves 0. 97 Spearman correlation with real evaluation rankings, significantly outperforming baseline simulators (0. 4 and -0. 05), and successfully predicts relative performance trends across both MIND benchmark and real customer datasets. Production environment validation shows consistent alignment between simulated metrics and real click-through rate (CTR) improvements.

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IJCAI Conference 2016 Conference Paper

Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations

Ragavendran Gopalakrishnan
Arpita Biswas
Alefiya Lightwala
Skanda Vasudevan
Partha Dutta
Abhishek Tripathi

Effective placement of charging stations plays a key role in Electric Vehicle (EV) adoption. In the placement problem, given a set of candidate sites, an optimal subset needs to be selected with respect to the concerns of both (a) the charging station service provider, such as the demand at the candidate sites and the budget for deployment, and (b) the EV user, such as charging station reachability and short waiting times at the station. This work addresses these concerns, making the following three novel contributions: (i) a supervised multi-view learning framework using Canonical Correlation Analysis (CCA) for demand prediction at candidate sites, using multiple datasets such as points of interest information, traffic density, and the historical usage at existing charging stations; (ii) a mixed-packing-and-covering optimization framework that models competing concerns of the service provider and EV users; (iii) an iterative heuristic to solve these problems by alternately invoking knapsack and setcover algorithms. The performance of the demand prediction model and the placement optimization heuristic are evaluated using real world data.

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ICLR Conference 2014 Conference Paper

Group-sparse Embeddings in Collective Matrix Factorization

Arto Klami
Guillaume Bouchard
Abhishek Tripathi

CMF is a technique for simultaneously learning low-rank representations based on a collection of matrices with shared entities. A typical example is the joint modeling of user-item, item-property, and user-feature matrices in a recommender system. The key idea in CMF is that the embeddings are shared across the matrices, which enables transferring information between them. The existing solutions, however, break down when the individual matrices have low-rank structure not shared with others. In this work we present a novel CMF solution that allows each of the matrices to have a separate low-rank structure that is independent of the other matrices, as well as structures that are shared only by a subset of them. We compare MAP and variational Bayesian solutions based on alternating optimization algorithms and show that the model automatically infers the nature of each factor using group-wise sparsity. Our approach supports in a principled way continuous, binary and count observations and is efficient for sparse matrices involving missing data. We illustrate the solution on a number of examples, focusing in particular on an interesting use-case of augmented multi-view learning.

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