DropoutNet: Addressing Cold Start in Recommender Systems
Authors: Maksims Volkovs, Guangwei Yu, Tomi Poutanen
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically we demonstrate state-of-the-art accuracy on publicly available benchmarks. To validate the proposed approach, we conducted extensive experiments on two publicly available datasets: Cite ULike [21] and the ACM Rec Sys 2017 challenge dataset [2]. Warm and cold start recall@100 results are shown in Table 1. |
| Researcher Affiliation | Industry | Maksims Volkovs layer6.ai maks@layer6.ai Guangwei Yu layer6.ai guang@layer6.ai Tomi Poutanen layer6.ai tomi@layer6.ai |
| Pseudocode | Yes | Algorithm 1: Learning Algorithm |
| Open Source Code | Yes | Code is available at https://github.com/layer6ai-labs/Dropout Net. |
| Open Datasets | Yes | To validate the proposed approach, we conducted extensive experiments on two publicly available datasets: Cite ULike [21] and the ACM Rec Sys 2017 challenge dataset [2]. [21] refers to 'C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In Conference on Knowledge Discovery and Data Mining, 2011.' and [2] refers to 'F. Abel, Y. Deldjo, M. Elahi, and D. Kohlsdorf. Recsys challenge 2017. http://2017. recsyschallenge.com, 2017.' |
| Dataset Splits | No | The paper details training and test splits but does not explicitly mention a dedicated validation set or its size/proportion for hyperparameter tuning. For evaluation, it mentions 'Fold 1 from [21]' for Cite ULike, which implies pre-defined splits, but no specific 'validation' split for their own experiments is detailed. |
| Hardware Specification | Yes | All experiments were conducted on a server with 20-core Intel Xeon CPU E5-2630 CPU, Nvidia Titan X GPU and 128GB of RAM. |
| Software Dependencies | No | The paper mentions using 'Tensor Flow library [1]' but does not specify a version number for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | All DNN models are trained with mini batches of size 100, fixed learning rate and momentum of 0.9. Using τ to denote the dropout rate, for each batch we randomly select τ batch size users and items. For our model we found that 1-hidden layer architectures with 500 hidden units and tanh activations gave good performance. We follow the approach of [6] and use a pyramid structure where the network gradually compresses the input witch each successive layer. For all architecture we use fully connected layers with batch norm [14] and tanh activation functions; other activation functions such as Re LU and sigmoid produced significantly worse results. We use the three layer model in all experiments. |