Combating the Cold Start User Problem in Model Based Collaborative Filtering

Understand

For tackling the well known cold-start user problem in model-based recommender systems, one approach is to recommend a few items to a cold-start user and use the feedback to learn a profile.

• The learned profile can then be used to make good recommendations to the cold user.
• In the absence of a good initial profile, the recommendations are like random probes, but if not chosen judiciously, both bad recommendations and too many recommendations may turn off a user.
• We formalize the cold-start user problem by asking what are the $b$ best items we should recommend to a cold-start user, in order to learn her profile most accurately, where $b$, a given budget, is typically a small number.