The cold-start is the arch-enemy of recommendation systems. It can affect both users and items.
For users, the cold-start means when the system has limited or no information on the customer’s behavior and preferences.
The item cold-start represents the lack of user interactions with the data upon which item-to-item relations can be drawn (we still have the metadata, though, but that won’t often suffice for truly fine-tuned recommendations).
The item cold-start is an obvious domain for the aforementioned content-based approach as it makes the system less reliant on transactional and interactional data. However, creating meaningful personalized experiences for new users is a much trickier problem that cannot necessarily be solved by simply gathering more information on them. It is quite typical –especially in the case of eCommerce sites or online marketplaces with wide product portfolios –that customers visit a website with completely different goals over time.
First they come to buy a microwave, but the next time they’re looking for a mobile phone. In this scenario, the data gathered in their first session is not relevant to the second. An intriguing approach to tackling the user cold-start problem is session based or item-to-session recommendations. This roughly means that insteadof relying on the whole interactional history of customers, the system splits this data into separate sessions.
The model capturing the users’ interests then builds on session-specific clickstreams. It then analyses this information in real time and triesto predict what the customer is looking for based on data solely from that session.