# Incorporating Recklessness to Collaborative Filtering based Recommender Systems

arXiv: https://arxiv.org/abs/2308.02058 · HTML: https://mlsanity.com/paper/2308.02058/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Understanding Echo Chambers in E-commerce Recommender Systems](https://mlsanity.com/paper/2007.02474/) (2020)
- [Providing reliability in Recommender Systems through Bernoulli Matrix Factorization](https://mlsanity.com/paper/2006.03481/) (2020)
- [Movie Recommendation System using Sentiment Analysis from Microblogging Data](https://mlsanity.com/paper/1811.10804/) (2018)

## Abstract (arXiv)

Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more reliable we desire the forecasts, the more conservative the decision will be and thus, the fewer items will be recommended. This causes a detriment to the predictive capability of the system, as it is only able to estimate potential interest in items for which there is a consensus in their evaluation, rather than being able to estimate potential interest in any item. In this paper, we propose the inclusion of a new term in the learning process of matrix factorization-based recommender systems, called recklessness, that takes into account the variance of the output probability distribution of the predicted ratings. In this way, gauging this recklessness measure we can force more spiky output distribution, enabling the control of the risk level desired when making decisions about the reliability of a prediction. Experimental results demonstrate that recklessness not only allows for risk regulation but also improves the quantity and quality of predictions provided by the recommender system.