Improving Recommendation Relevance by simulating User Interest
This work addresses the need for timely recommendations in online systems, but it appears incremental as it builds on existing similarity-based approaches with a specific algorithmic tweak.
The paper tackles the problem of maintaining time-sensitive similarity measures in online item-to-item recommendation systems by proposing an iterative reduction of ranks for inactive items to improve recommendation relevance. The result is a straightforward and transparent method for maintaining recency, with the basic idea being patented in the context of online recommendation systems.
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.