Abstract: Near real-time Updates for Cooccurrence-based Recommenders
Most recommendation algorithms are inherently batch oriented and require all relevant history to be processed. In some contexts such as music, this does not cause significant problems because waiting a day or three before recommendations are available for new items doesn’t significantly change their impact. In other contexts, the value of items drops precipitously with time so that recommending day-old items has little value to users.
In this talk, I will describe how a large-scale multi-modal cooccurrence recommender can be extended to include near real-time updates. In addition, I will show how these real-time updates are compatible with delivery of recommendations via search engines.