Cold start (recommender systems)
Cold start is a potential problem in computer-based information systems which involves a degree of automated data modelling.
Specifically, it concerns the issue that the system cannot draw any inferences for users or items about which it has not yet gathered sufficient information.
Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (e-commerce, films, music, books, news, images, web pages) that are likely of interest to the user.
Depending on the system, the user can be associated to various kinds of interactions: ratings, bookmarks, purchases, likes, number of page visits etc.
This constitutes a problem mainly for collaborative filtering algorithms due to the fact that they rely on the item's interactions to make recommendations.
Collaborative filtering algorithms are the most affected as without interactions no inference can be made about the user's preferences.
[10] Due to the high number of recommender algorithms available as well as system type and characteristics, many strategies to mitigate the cold-start problem have been developed.
The main approach is to rely on hybrid recommenders, in order to mitigate the disadvantages of one category or model by combining it with another.
[14] In case of new users, if no demographic feature is present or their quality is too poor, a common strategy is to offer them non-personalized recommendations.
This means that they could be recommended simply the most popular items either globally or for their specific geographical region or language.
One of the available options when dealing with cold users or items is to rapidly acquire some preference data.
In both cases, the cold start problem would imply that the user has to dedicate an amount of effort using the system in its 'dumb' state – contributing to the construction of their user profile – before the system can start providing any intelligent recommendations.
The main goal of active learning is to guide the user in the preference elicitation process in order to ask him to rate only the items that for the recommender point of view will be the most informative ones.
[22] The cold start problem may be overcome by introducing an element of collaboration amongst agents assisting various users.
This way, novel situations may be handled by requesting other agents to share what they have already learnt from their respective users.
[22] In recent years more advanced strategies have been proposed, they all rely on machine learning and attempt to merge the content and collaborative information in a single model.
One example of this approaches is called attribute to feature mapping[23] which is tailored to matrix factorization algorithms.
As an example consider the James Bond movie series, the main actor changed many times during the years, while some did not, like Lois Maxwell.
Some of them learn feature weight by exploiting directly the user's interactions with items, like FBSM.
