Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
4-1-2021 12:00 AM
End Date
9-1-2021 12:00 AM
Description
The cold-start problem has become a significant challenge in recommender systems. To solve this problem, most approaches use various user-side data and combine them with item-side information in their systems design. However, when such user data is not available, those methods become unfeasible. We provide a novel recommender system design approach which is based on two-stage decision heuristics. By utilizing only the item-side characteristics we first identify the structure of the final choice set and then generate it using stochastic and deterministic approaches.
Use of clustering for consideration set modeling in recommender systems
Online
The cold-start problem has become a significant challenge in recommender systems. To solve this problem, most approaches use various user-side data and combine them with item-side information in their systems design. However, when such user data is not available, those methods become unfeasible. We provide a novel recommender system design approach which is based on two-stage decision heuristics. By utilizing only the item-side characteristics we first identify the structure of the final choice set and then generate it using stochastic and deterministic approaches.
https://aisel.aisnet.org/hicss-54/in/electronic_marketing/6