Improving Recommendation Relevance by simulating User Interest

Alexander Kushkuley, Joshua Correa
2/3/2023
math.NAcs.IRcs.LG

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

Most if not all on-line item-to-item recommendation systems rely on estimation of a distance like measure (rank) of similarity between items. For on-line recommendation systems, time sensitivity of this similarity measure is extremely important. We observe that recommendation "recency" can be straightforwardly and transparently maintained by iterative reduction of ranks of inactive items. The paper briefly summarizes algorithmic developments based on this self-explanatory observation. The basic idea behind this work is patented in a context of online recommendation systems.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Measuring Recency Bias In Sequential Recommendation Systems

82% match2024

HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling

81% match1 repo6252024

FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning

81% match2024

Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders

80% match2023

Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model

80% match2024

Handling Large-scale Cardinality in building recommendation systems

80% match2024

Cite this paper

@article{kushkuley2023improving,
  title  = {Improving Recommendation Relevance by simulating User Interest},
  author = {Alexander Kushkuley and Joshua Correa},
  year   = {2023},
  eprint = {2302.01522},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.01522v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2302.01522.svg)](https://codeofpaper.com/paper/2302.01522)

Discussion