Difference between revisions of "Cold-start problem"
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Zeno Gantner (talk | contribs) (one way of defining cold-start problems - add your definition!) |
Zeno Gantner (talk | contribs) |
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One can distinguish two different types of cold-start scenarios: | One can distinguish two different types of cold-start scenarios: | ||
| − | * strict cold-start scenarios, where no collaborative information is available about the user/item in question, | + | * ''strict'' cold-start scenarios, where no collaborative information is available about the user/item in question, and |
| − | * | + | * ''weak'' cold-start scenarios, where only some collaborative information is available about the user/item in question. |
Typical ways of dealing with the cold-start problem are to use additional (non-collaborative) information about the new user/item, | Typical ways of dealing with the cold-start problem are to use additional (non-collaborative) information about the new user/item, | ||
e.g. [[content-based filtering]] (for new items), or trying to get the necessary preference information by interaction with user, e.g. using an [[active learning]] approach. | e.g. [[content-based filtering]] (for new items), or trying to get the necessary preference information by interaction with user, e.g. using an [[active learning]] approach. | ||
| + | |||
| + | == External links == | ||
| + | * [[Wikipedia: Cold start]] | ||
| + | |||
| + | [[Category: Task]] | ||
Latest revision as of 08:57, 26 September 2011
When past data is scarce, e.g. after the initial start of a recommender system, or if a new user or item is added to the system, collaborative filtering methods are usually not able to come up with good predictions. Dealing with such situations is called the cold-start problem.
One can distinguish two different types of cold-start scenarios:
- strict cold-start scenarios, where no collaborative information is available about the user/item in question, and
- weak cold-start scenarios, where only some collaborative information is available about the user/item in question.
Typical ways of dealing with the cold-start problem are to use additional (non-collaborative) information about the new user/item, e.g. content-based filtering (for new items), or trying to get the necessary preference information by interaction with user, e.g. using an active learning approach.
