Related papers: Improving Recommendation Relevance by simulating User Interest

Improving Recommendation Relevance by simulating User Interest

URL: http://arxiv.org/abs/2302.01522v1
Date: Fri, 3 Feb 2023 03:35:28 GMT
Title: Improving Recommendation Relevance by simulating User Interest
Authors: Alexander Kushkuley and Joshua Correa
Abstract summary: We observe that recommendation "recency" can be straightforwardly and transparently maintained by iterative reduction of ranks of inactive items. The basic idea behind this work is patented in a context of online recommendation systems.
Score: 77.34726150561087
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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