Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Non-Linear Label Ranking for Large-Scale Prediction of Long-Term User Interests
Authors: Nemanja Djuric, Mihajlo Grbovic, Vladan Radosavljevic, Narayan Bhamidipati, Slobodan Vucetic
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a real-world advertising data set with more than 3.2 million users show that the proposed algorithm outperforms the existing solutions in terms of both rank loss and top-K retrieval performance, strongly suggesting the benefit of using the proposed model on large-scale ranking problems. |
| Researcher Affiliation | Collaboration | Yahoo! Labs, Sunnyvale, CA, USA, EMAIL Temple University, Philadelphia, PA, USA, EMAIL |
| Pseudocode | No | The paper describes algorithms using mathematical formulas and textual explanations, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper mentions using and modifying existing tools like Vowpal Wabbit and Budgeted SVM (Djuric et al. 2014) but does not provide a link or explicit statement about the availability of their specific modified code or the implementation of AMM-rank. |
| Open Datasets | No | The data set that was used in the empirical evaluation was generated using the information about users online activities collected at Yahoo servers. |
| Dataset Splits | Yes | Performance of the competing methods in terms of ϵdis, following 5-fold cross-validation, is reported in Table 1. |
| Hardware Specification | No | We note that, other than IB-Mal, the methods are very efficient, obtaining training and test times of less than 10 minutes on a regular machine. |
| Software Dependencies | No | We used Vowpal Wabbit package1 for logistic regression, Budgeted SVM (Djuric et al. 2014) for AMM, that we also modified to implement AMM-rank. ...used the default parameters from Budgeted SVM package for AMM-rank. The paper mentions software names but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We set ν(i) = 1, i = 1, . . . , L, and used the default parameters from Budgeted SVM package for AMM-rank, with the exception of the λ parameter which, together with competitors parameters, was configured through cross-validation on a small held-out set; this resulted in k = 10 for IB-Mal. |