A new approach to calibrating recommendations to user interests
Users’ interests are multi-faceted and representing different aspects of users’ interest in their recommendations is an important factor for recommender systems to help users navigate more quickly to items or content they may be interested in. This property is often referred to as the calibration problem and has achieved significant attention recently. Calibration is particularly important given that a sole optimization towards accuracy can often lead to the user’s minority interests being dominated by their main interests, or by a few overall popular items, in the recommendations they receive. In this work, we propose a novel approach based on a minimum-cost flow through a graph for generating accurate and calibrated recommendations.
Calibration in Recommender Systems
Recommender systems often optimize for the most relevant items to the user. Suppose a user has listened to a lot of pop music, some jazz music and also some podcasts. The recommender algorithm may return a list of recommendations that are all pop music (ignoring other types of music and also ignoring podcasts) or a list containing all music (ignoring podcasts) as those might be the most relevant to the user according to the objective function. Would such a recommendation list be useful to the user? Calibration in recommendation refers to the fact that a good recommendation list should reflect various aspects of the user’s interest ideally in the right proportion.
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That’s a great point about user interests being diverse and sometimes overshadowed by their main hobbies in recommendation systems. I’ve noticed this myself on music streaming platforms—if I listen to one genre a lot, the recommendations tend to ignore my less frequent interests. I wish calibration could work better, like in games such as Geometry Dash, where there are recommended levels from different categories, helping me find new favorites I might have missed.
This is a really interesting point about recommender systems! I’ve definitely experienced the “pop music only” phenomenon. It can get frustrating. I find myself searching for new podcast episodes myself because the algorithms seem to forget I listen to them. Sometimes I think these systems need a random “surprise me” button. Speaking of things that require precision and quick reflexes, for a totally different type of challenge, you might enjoy Geometry Dash. It’s a rhythm-based platformer that’s surprisingly addictive!