Ice-Breaking: Mitigating Cold-Start Recommendation Problem by Rating Comparison
Authors: Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, Jian Lu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios. |
| Researcher Affiliation | Academia | Jingwei Xu, Yuan Yao State Key Laboratory for Novel Software Technology, China {jingwei.xu,yyao}@smail.nju.edu.cn Hanghang Tong Arizona State University, USA hanghang.tong@asu.edu Xianping Tao and Jian Lu State Key Laboratory for Novel Software Technology, China {txp,lj}@nju.edu.cn |
| Pseudocode | Yes | Algorithm 1: Fast learning RAPARE-MF |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use two real, benchmark data sets: Movie Lens1 and Each Movie. The Movie Lens data contains 1M ratings that are collected and published from the Movie Lens website2. Each Movie is organized by HP/Compaq Research. |
| Dataset Splits | No | The paper describes a dynamic evaluation protocol for cold-start scenarios but does not specify a conventional train/validation/test split for the entire dataset or an explicit validation set. |
| Hardware Specification | Yes | All the experiments are run on a Macbook Pro. The machine has four 2.2GHz Intel i7 Cores and 8GB memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software dependencies used in the experiments. |
| Experiment Setup | Yes | We summarize the overall evaluation protocol for the coldstart users with the following descriptions. 1. Setup the cold-start user scenario Randomly choose 25% users (as cold-start users), put them into set Uc, and put all their ratings into the set Ec. The rest users are considered as warm users. Train the model for warm users via the MF method [Koren et al., 2009]. After this step, we can obtain the latent profiles of warm users and the latent profiles for all items 2. Evaluate the cold-start user scenario Create an empty set Rc for n = 1, ..., 10 do: for each cold-start user u Uc do: * randomly pick up3 one of his ratings from the Ec and move it to Rc * learn the latent profile of user u by the proposed method RAPARE-MF * calculate the error between the predicted result and the actual result for the rest of u s ratings in the Ec calculate the error over all cold-start users |