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. All three categories of cold-start (new community, new item, and new user) have in common the lack of user interactions and presents some commonalities in the strategies available to address them. A common strategy when dealing with new items is to couple a
collaborative filtering recommender, for warm items, with a
content-based filtering recommender, for cold-items. While the two algorithms can be combined in different ways, the main drawback of this method is related to the poor recommendation quality often exhibited by content-based recommenders in scenarios where it is difficult to provide a comprehensive description of the item characteristics. This may be done either explicitly (by querying the user) or implicitly (by observing the user's behaviour). 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. For example
MovieLens, a web-based
recommender system for movies, asks the user to rate some movies as a part of the registration. While preference elicitation strategy are a simple and effective way to deal with new users, the additional requirements during the registration will make the process more time-consuming for the user. Moreover, the quality of the obtained preferences might not be ideal as the user could rate items they had seen months or years ago or the provided ratings could be almost random if the user provided them without paying attention just to complete the registration quickly. The construction of the user's profile may also be automated by integrating information from other user activities, such as browsing histories or social media platforms. If, for example, a user has been reading information about a particular
music artist from a media portal, then the associated recommender system would automatically propose that artist's releases when the user visits the music store. A variation of the previous approach is to automatically assign ratings to new items, based on the ratings assigned by the community to other similar items. Item similarity would be determined according to the items' content-based characteristics.
Personality characteristics of the user can be identified using a personality model such as
five factor model (FFM). Another of the possible techniques is to apply
active learning (machine learning). 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. This is done by analysing the available data and estimating the usefulness of the data points (e.g., ratings, interactions). As an example, say that we want to build two clusters from a certain cloud of points. As soon as we have identified two points each belonging to a different cluster, which is the next most informative point? If we take a point close to one we already know we can expect that it will likely belong to the same cluster. If we choose a point which is in between the two clusters, knowing which cluster it belongs to will help us in finding where the boundary is, allowing to classify many other points with just a few observations. The cold start problem is also exhibited by
interface agents. Since such an agent typically learns the user's preferences implicitly by observing patterns in the user's behaviour – "watching over the shoulder" – it would take time before the agent may perform any adaptations personalised to the user. Even then, its assistance would be limited to activities which it has formerly observed the user engaging in. 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. which is tailored to
matrix factorization algorithms. The basic idea is the following. A matrix factorization model represents the user-item interactions as the product of two rectangular matrices whose content is learned using the known interactions via machine learning. Each user will be associated to a row of the first matrix and each item with a column of the second matrix. The row or column associated to a specific user or item is called
latent factors. When a new item is added it has no associated latent factors and the lack of interactions does not allow to learn them, as it was done with other items. If each item is associated to some features (e.g. author, year, publisher, actors) it is possible to define an embedding function, which given the item features estimates the corresponding item latent factors. The embedding function can be designed in many ways and it is trained with the data already available from warm items. Alternatively, one could apply a group-specific method. A group-specific method further decomposes each latent factor into two additive parts: One part corresponds to each item (and/or each user), while the other part is shared among items within each item group (e.g., a group of movies could be movies of the same genre). Then once a new item arrives, we can assign a group label to it, and approximates its latent factor by the group-specific part (of the corresponding item group). Therefore, although the individual part of the new item is not available, the group-specific part provides an immediate and effective solution. The same applies for a new user, as if some information is available for them (e.g. age, nationality, gender) then his/her latent factors can be estimated via an embedding function or a group-specific latent factor.
Hybrid feature weighting Another recent approach which bears similarities with feature mapping is building a hybrid
content-based filtering recommender in which features, either of the items or of the users, are weighted according to the user's perception of importance. In order to identify a movie that the user could like, different attributes (e.g. which are the actors, director, country, title) will have different importance. 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. Therefore, her presence will probably be a better identifier of that kind of movie than the presence of one of the various main actors. Although various techniques exist to apply feature weighting to user or item features in
recommender systems, most of them are from the
information retrieval domain like
tf–idf,
Okapi BM25, only a few have been developed specifically for recommenders. Hybrid feature weighting techniques in particular are tailored for the recommender system domain. Some of them learn feature weight by exploiting directly the user's interactions with items, like FBSM.
Differentiating regularization weights The above methods rely on affiliated information from users or items. Recently, another approach mitigates the cold start problem by assigning lower constraints to the latent factors associated with the items or users that reveal more information (i.e., popular items and active users), and set higher constraints to the others (i.e., less popular items and inactive users). It is shown that various recommendation models benefit from this strategy. Differentiating regularization weights can be integrated with the other cold start mitigating strategies. ==See also==