Papers by Shreyas Kadekodi
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021
Recommender systems are complex. They integrate the individual needs of users with the characteri... more Recommender systems are complex. They integrate the individual needs of users with the characteristics of particular domains of application which may span items from large and potentially heterogeneous collections. Extensive experimentation is required to understand the multidimensional properties of recommendation algorithms and the fit between algorithm and application. librec-auto is a tool that automates many aspects of off-line batch recommender system experimentation. It has a large library of state-of-the-art and historical recommendation algorithms and a wide variety of evaluation metrics. It further supports the study of diversity and fairness in recommendation through the integration of re-ranking algorithms and fairness-aware metrics. It supports declarative configuration for reproducible experiment management and supports multiple forms of hyperparameter optimization. CCS CONCEPTS • Information systems → Recommender systems; Relevance assessment; • Human-centered computing → Collaborative filtering; • Social and professional topics → User characteristics; • Software and its engineering → Software libraries and repositories.
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Papers by Shreyas Kadekodi