Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback
Authors: Yong Liu, Lifan Zhao, Guimei Liu, Xinyan Lu, Peng Gao, Xiao-Li Li, Zhihui Jin
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed method has been demonstrated by extensive experiments on real datasets, compared with the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Institute for Infocomm Research, A*STAR, Singapore NTUC Link Pte. Ltd., Singapore Tencent Social Ads, China liuysc@acm.org, zhao0145@e.ntu.edu.sg, {liug, xlli}@i2r.a-star.edu.sg, {xinyanlu, stephengao, rickjin}@tencent.com |
| Pseudocode | Yes | Algorithm 1: Variational Inference for DBLMF |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We extracted four datasets from Movielens-20M1 for evaluation. Following [Pan and Chen, 2013; Liu et al., 2015], we kept ratings larger than 3 as implicit feedback. Dataset 2005-3Y was extracted using the following conditions: 1) only ratings between 2005-01-01 and 2010-12-31 were included; 2) ratings before and on 2007-12-31 were used for training, and ratings after 2007-12-31 were used for testing; and 3) only users with at least 5 ratings in training data and at least one rating in testing data were included. Dataset 2002-6Y was generated in a similar way by using the rating data between 2002-01-01 and 2007-12-31 as training data, and the rating data between 2008-01-01 to 2010-12-31 as testing data. As the proposed algorithms focus on modeling users preferences over time, it is better to have users activities over a long period. To this end, we further restrict that users must have at least one rating before 2005-12-31 on dataset 2005-3Y, and at least one rating before 2002-12-31 on dataset 2002-6Y. This leads to two more datasets: 2005-3Y-S and 2002-6Y-S. Table 1 summarizes the statistics of these datasets. |
| Dataset Splits | No | ratings before and on 2007-12-31 were used for training, and ratings after 2007-12-31 were used for testing |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For matrix factorization methods, we set the dimensionality of the latent space d at 32. In DBLMF and s DBLMF, we set σ0 = 10, a0 = a1 = 10 4, and b0 = b1 = 10 4. Moreover, we define the dynamic weight as wt ij = 1 + δrt ij, where rt ij denotes the rating that ui assigned to vj at time t, and we empirically set δ = 1 in the experiments. |