Deconvolving Feedback Loops in Recommender Systems
Authors: Ayan Sinha, David F. Gleich, Karthik Ramani
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use this metric on synthetic and real-world datasets to (1) identify the extent to which the recommender system affects the final rating matrix, (2) rank frequently recommended items, and (3) distinguish whether a user s rated item was recommended or an intrinsic preference. We tested our approach for deconvolving feedback loops on synthetic RS, and designed a metric to identify the ratings most affected by the RS. We then use the same automated technique to study real-world ratings data, and find that the metric is able to identify items influenced by a RS. |
| Researcher Affiliation | Academia | Ayan Sinha Purdue University sinhayan@mit.edu David F. Gleich Purdue University dgleich@purdue.edu Karthik Ramani Purdue University ramani@purdue.edu |
| Pseudocode | Yes | Algorithm 1: Deconvolving Feedback Loops |
| Open Source Code | No | The paper does not provide any specific links to source code for the methodology or state that code is available. |
| Open Datasets | Yes | Table 1 lists all the datasets we use to validate our approach for deconvolving a RS (from [21, 4, 13]). |
| Dataset Splits | No | The paper describes how synthetic data was generated and the overall evaluation process (e.g., ROC curves), but it does not provide specific details on how the real-world datasets were split into training, validation, and test sets for the experiments. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks) used in the experiments. |
| Experiment Setup | Yes | In our experiment, we draw au N(3, 1), bu N(0.5, 0.5), tu N(0.1, 1), and ηu,i ϵN(0, 1)... We fix the number of iterative updates to be 10, r to be 10 and the resulting rating matrix is Robs. We use α = 1 in all experiments because it models the case when the recommender effects are strong and thus produces the highest discriminative effect between the observed and true ratings (see Figure 2 f). |