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 [1].
Modeling Users' Dynamic Preference for Personalized Recommendation
Authors: Xin Liu
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted over two real datasets demonstrate that our approach outperforms the state-of-the-art recommendation models by at least 42.46% and 66.14% in terms of precision and Mean Reciprocal Rank respectively. |
| Researcher Affiliation | Academia | Xin Liu Institute for Infocomm Research (I2R) Singapore EMAIL |
| Pseudocode | No | The paper describes the model in text and mathematical equations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | Yes | The ο¬rst dataset we used was collected from Delicious (http://www.delicious.com). The data [Cantador et al., 2011] contains 1,867 users and 69,226 URLs... We also used the data collected from Last.fm (http://www. lastfm.com). The data [Cantador et al., 2011] records the information of 1,892 users listening history... |
| Dataset Splits | Yes | The parameters of MF based models such as latent factor vector dimensionality, learning rate, regularization terms are determined by 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The hyperparameters h, Ξ» and Ο are initialized to 1. ... The parameters of MF based models such as latent factor vector dimensionality, learning rate, regularization terms are determined by 5-fold cross validation. ... for both datasets, 10 topics achieve the highest precision and MRR. In the following experiments, we use 10 topics for all top modeling based models. |