Cold start
Encyclopedia
Cold start is a potential problem in computer-based
information systems
which involve 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.
, music
, book
s, news
, image
s, web page
s) that are likely of interest to the user. Typically, a recommender system compares the user's profile to some reference characteristics. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering
approach).
In the content-based approach, the system must be capable of matching the characteristics of an item against relevant features in the user's profile. In order to do this, it must first construct a sufficiently-detailed model of the user's tastes and preferences through preference elicitation
. 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.
In the collaborative filtering approach, the recommender system would identify users who share the same preferences (e.g. rating patterns) with the active user, and propose items which the like-minded users favoured (and the active user has not yet seen). Due to the cold start problem, this approach would fail to consider items which no-one in the community has rated previously.
The cold start problem is also exhibited by interface
agents
. Since such an agent typically learn 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.
In recommender systems, the cold start problem is often reduced by adopting a hybrid approach between content-based matching and collaborative filtering. New items (which have not yet received any ratings from the community) would be assigned a rating automatically, based on the ratings assigned by the community to other similar items. Item similarity would be determined according to the items' content-based characteristics.
The construction of the user's profile may be automated by integrating information from other user activities, such as browsing histories. 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.
Information technology
Information technology is the acquisition, processing, storage and dissemination of vocal, pictorial, textual and numerical information by a microelectronics-based combination of computing and telecommunications...
information systems
Information systems
Information Systems is an academic/professional discipline bridging the business field and the well-defined computer science field that is evolving toward a new scientific area of study...
which involve a degree of automated data modelling. Specifically, it concerns the issue that the system cannot draw any inferences for users
User (computing)
A user is an agent, either a human agent or software agent, who uses a computer or network service. A user often has a user account and is identified by a username , screen name , nickname , or handle, which is derived from the identical Citizen's Band radio term.Users are...
or items about which it has not yet gathered sufficient information.
Systems affected
The cold start problem is most prevalent in recommender systems. Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (moviesFilm
A film, also called a movie or motion picture, is a series of still or moving images. It is produced by recording photographic images with cameras, or by creating images using animation techniques or visual effects...
, music
Music
Music is an art form whose medium is sound and silence. Its common elements are pitch , rhythm , dynamics, and the sonic qualities of timbre and texture...
, book
Book
A book is a set or collection of written, printed, illustrated, or blank sheets, made of hot lava, paper, parchment, or other materials, usually fastened together to hinge at one side. A single sheet within a book is called a leaf or leaflet, and each side of a leaf is called a page...
s, news
News
News is the communication of selected information on current events which is presented by print, broadcast, Internet, or word of mouth to a third party or mass audience.- Etymology :...
, image
Image
An image is an artifact, for example a two-dimensional picture, that has a similar appearance to some subject—usually a physical object or a person.-Characteristics:...
s, web page
Web page
A web page or webpage is a document or information resource that is suitable for the World Wide Web and can be accessed through a web browser and displayed on a monitor or mobile device. This information is usually in HTML or XHTML format, and may provide navigation to other web pages via hypertext...
s) that are likely of interest to the user. Typically, a recommender system compares the user's profile to some reference characteristics. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering
Collaborative filtering
Collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets...
approach).
In the content-based approach, the system must be capable of matching the characteristics of an item against relevant features in the user's profile. In order to do this, it must first construct a sufficiently-detailed model of the user's tastes and preferences through preference elicitation
Preference elicitation
Preference elicitation refers to the problem of developing a decision support system capable of generating recommendations to a user, thus assisting him in decision making. It is important for such a system to model user's preferences accurately, find hidden preferences and avoid redundancy. This...
. 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.
In the collaborative filtering approach, the recommender system would identify users who share the same preferences (e.g. rating patterns) with the active user, and propose items which the like-minded users favoured (and the active user has not yet seen). Due to the cold start problem, this approach would fail to consider items which no-one in the community has rated previously.
The cold start problem is also exhibited by interface
User interface
The user interface, in the industrial design field of human–machine interaction, is the space where interaction between humans and machines occurs. The goal of interaction between a human and a machine at the user interface is effective operation and control of the machine, and feedback from the...
agents
Software agent
In computer science, a software agent is a piece of software that acts for a user or other program in a relationship of agency, which derives from the Latin agere : an agreement to act on one's behalf...
. Since such an agent typically learn 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.
Solutions
In scenarios involving interface agents, 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.In recommender systems, the cold start problem is often reduced by adopting a hybrid approach between content-based matching and collaborative filtering. New items (which have not yet received any ratings from the community) would be assigned a rating automatically, based on the ratings assigned by the community to other similar items. Item similarity would be determined according to the items' content-based characteristics.
The construction of the user's profile may be automated by integrating information from other user activities, such as browsing histories. If, for example, a user has been reading information about a particular music artist
Musician
A musician is an artist who plays a musical instrument. It may or may not be the person's profession. Musicians can be classified by their roles in performing music and writing music.Also....* A person who makes music a profession....
from a media portal, then the associated recommender system would automatically propose that artist's releases when the user visits the music store.