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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Memory Augmented Neural Model for Incremental Session-based Recommendation
Authors: Fei Mi, Boi Faltings
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we show that MAN boosts the performance of different neural methods and achieves state-of-the-art. |
| Researcher Affiliation | Academia | Fei Mi and Boi Faltings Artificial Intelligence Laboratory, Ecole Polytechnique F ed erale de Lausanne (EPFL) fei.mi@epfl.ch, boi.faltings@epfl.ch |
| Pseudocode | Yes | Algorithm 1 Memory Augmented Neural Recommender |
| Open Source Code | No | The paper mentions using the FAISS open-source library (https://github.com/facebookresearch/faiss) but does not state that the code for the described methodology (MAN) is open-source or provided. |
| Open Datasets | Yes | YOOCHOOSE: This is a public dataset for Rec Sys Challenge 2015.2 It contains click-streams on an e-commerce site over 6 months. 2http://2015.recsyschallenge.com/challenge.html DIGINETICA: This dataset contains click-streams data on another e-commerce site over a span of 5 months for CIKM Cup 2016.3 3http://cikm2016.cs.iupui.edu/cikm-cup |
| Dataset Splits | Yes | The last 10% of the training data based on time is used as the validation set. |
| Hardware Specification | Yes | Both models are trained using a NVIDIA TITAN X GPU with 12GB memory. |
| Software Dependencies | No | The paper mentions using the "FAISS open-source library" but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | During training, hidden layer size of GRU4Rec and NARM is set to 100, and the item embedding size of NARM is set to 50; the batch size is set to 512 and 30 epochs are trained for both models. ... Learning rates for neural models are 5e-4 and 1e-4 for YOOCHOOSE and DIGINETICA, and the learning rate to update the gating network is 1e-3. ... The number of nearest neighbors of MAN is set to 50 for YOOCHOOSE and 100 for DIGINETICA. |