SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation
Authors: Chenwei Cai, Ruining He, Julian McAuley
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To fully evaluate the effectiveness of our proposed model, we perform extensive experiments on a series of real-world datasets and compare against state-of-the-art sequentially- and socially-aware methods. |
| Researcher Affiliation | Academia | Chenwei Cai, Ruining He, Julian Mc Auley University of California, San Diego {cwcai, r4he, jmcauley}@ucsd.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We experiment on four datasets, each comprising a large corpus of user feedback, timestamps, as well as social relations (i.e. trusts ). All datasets are available online. |
| Dataset Splits | Yes | From each user one positive item Tu I+ u is held out for testing, and another item Vu I+ u is held out for validation. The test item Tu is chosen to be the most recent item according to user u s feedback history, while the validation item Vu is the second most recent one. The rest of the data is used as the training set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For fair comparison, we set the dimensions of latent factors in all models to 20, i.e., for SPMC K1 = K2 = K3 = 20. We experimented with learning rates η {0.5, 0.05, 0.005} and regularization hyperparameters λ {1, 0.1, 0.01, 0.001}, selecting the values that resulted in the best performance on the validation set. We set α = 1 for all datasets (the sensitivity of α will be discussed later). For GBPR, the group size is set to 3 and ρ is set to 0.8. |