librec-auto: A Tool for Recommender Systems Experimentation

Sonboli, Nasim; Mansoury, Masoud; Guo, Ziyue; Kadekodi, Shreyas; Liu, Weiwen; Liu, Zijun; Schwartz, Andrew A.; Burke, Robin

doi:10.1145/3459637.3482006

Cited by 10 publications

(2 citation statements)

References 40 publications

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“…In recent years, introducing and implementing RS frameworks and libraries gained huge attention. Sonboli et al [20] proposed a recommendation framework titled Librec-auto for automating various aspects of offline batch RS experimentats. The framework covers a wide range of recommendation and re-ranking algorithms, along with various evaluation and fairness-aware metrics.…”

Section: Related Frameworkmentioning

confidence: 99%

“…Elliot [2], LensKit [6], LibRec [7], LibRec-auto [20], OpenRec [25], CaseRec [4], which mainly aim to reproduce various traditional recommender systems, deep learning-based recommender systems such as DeepRec [29], and multimodal RSs like Cornac [17], CAPRI is intended to provide contextually aware recommendation and evaluation in the POI domain. We have equipped our framework with state-of-the-art models, algorithms, well-known datasets for POI recommendations, and multi-dimensional evaluation criteria (accuracy, beyond-accuracy, and user-item fairness).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Rahmani

Naghiaei

Tourani

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

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Point-of-Interest (POI) recommendation systems have gained popularity for their unique ability to suggest geographical destinations, with the incorporation of contextual information such as time, location, and user-item interaction. Existing recommendation frameworks lack the contextual fusion required for POI systems. This paper presents CAPRI, a novel POI recommendation framework that effectively integrates context-aware models, such as GeoSoCa, LORE, and USG, and introduces a novel strategy for the efficient merging of contextual information. CAPRI integrates an evaluation module that expands the evaluation scope beyond accuracy to include novelty, personalization, diversity, and fairness. With an aim to establish a new industry standard for reproducible results in the realm of POI recommendation systems, we have made CAPRI openly accessible on GitHub, facilitating easy access and contribution to the continued development and refinement of this innovative framework. 1

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Section: Related Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Rahmani

Naghiaei

Tourani

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

View full text Add to dashboard Cite

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AI‐assisted research collaboration with open data for fair and effective response to call for proposals

Valluru,

Widener,

Srivastava

et al. 2024

AI Magazine

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Building teams and promoting collaboration are two very common business activities. An example of these are seen in the TeamingForFunding problem, where research institutions and researchers are interested to identify collaborative opportunities when applying to funding agencies in response to latter's calls for proposals. We describe a novel deployed system to recommend teams using a variety of Artificial Intelligence (AI) methods, such that (1) each team achieves the highest possible skill coverage that is demanded by the opportunity, and (2) the workload of distributing the opportunities is balanced among the candidate members. We address these questions by extracting skills latent in open data of proposal calls (demand) and researcher profiles (supply), normalizing them using taxonomies, and creating efficient algorithms that match demand to supply. We create teams to maximize goodness along a novel metric balancing short‐ and long‐term objectives. We evaluate our system in two diverse settings in US and India of researchers and proposal calls, at two different time instants about 1 year apart (total 4 settings), to establish generality of our approach, and deploy it at a major US university. We validate the effectiveness of our algorithms (1) quantitatively, by evaluating the recommended teams using a goodness score and find that more informed methods lead to recommendations of smaller number of teams and higher goodness, and (2) qualitatively, by conducting a large‐scale user study at a college‐wide level, and demonstrate that users overall found the tool very useful and relevant.

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Multi-sourced Integrated Ranking with Exposure Fairness

Liu,

Xia

et al. 2024

Lecture Notes in Computer Science

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librec-auto: A Tool for Recommender Systems Experimentation

Cited by 10 publications

References 40 publications

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

AI‐assisted research collaboration with open data for fair and effective response to call for proposals

Multi-sourced Integrated Ranking with Exposure Fairness

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