Exploiting Fine-Grained Skip Behaviors for Micro-Video Recommendation
Authors: Sanghyuck Lee, Sangkeun Park, Jaesung Lee
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrated that the proposed method outperformed three conventional methods across eight evaluation measures on two public datasets. ... As shown in Tables 2 and 3, the two experimental results demonstrate that the proposed method outperforms the comparison models across two different datasets. |
| Researcher Affiliation | Academia | Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea EMAIL |
| Pseudocode | No | The paper describes the proposed method in narrative text and mathematical formulations (Equations 1-20), but it does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement regarding the availability of source code or links to a code repository. |
| Open Datasets | Yes | The MVA dataset (Shang et al. 2023) was collected from a mobile app platform. ... The Kuai Rand-Pure dataset (Gao et al. 2022b), collected from the Kuaishou app, one of the largest video-sharing platforms in China, provides an unbiased sequential recommendation dataset. |
| Dataset Splits | Yes | The entire set of interactions is randomly sampled at a 6:2:2 ratio for each user to generate the training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | Yes | All models were implemented using Py Torch 2.3. |
| Experiment Setup | Yes | Each model was trained with a batch size of 1024 using the Adam W optimizer (Loshchilov and Hutter 2019) with a momentum of 0.9 and a weight decay of 1e-4. The learning rate starts at 1e-3 and decays to 1e-6 following a cosine annealing schedule. ... Early stopping is applied if the recall@3 on the validation set does not improve for five consecutive epochs. The feature dimension d is set to 128, and λ is set to 0.5. |