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 | Conference PDF | Archive PDF | Plain Text | 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, {nemanja, mihajlo, vladan, narayanb}@yahoo-inc.com Temple University, Philadelphia, PA, USA, vucetic@temple.edu |
| 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. |