Cold start

Cold start is a potential problem in computer-based information systems which involve a degree of automated data modeling . Specifically, it concerns the issue that the system can not draw any inferences for users or items about which it has not enough gathered sufficient information.

Systems affected

The cold start problem is most prevalent in recommender systems . Recommender systems form a specific kind of information filtering (IF) That technical Attempts to present information items ( movies , music , books , news , pictures , web pages ) That are Likely of interest to the user. Typically, a recommender system compares the user’s profile with some reference characteristics. These characteristics may be obtained 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 able to match the features of 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 behavior). 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.

In the collaborative filtering approach, the recommender system would identify users who share the same preferences with the active user, and proposes items which the user-minded users favored (and the active user has not seen). Due to the cold start problem, this approach would fail to consider which items in the community has rated previously. [1]

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 behavior – “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 formally observed the user engaging in. [2]

Solutions

There are several solutions that have been proposed to tackle the cold start problem. One of the effective solutions is to apply active learning (machine learning) techniques, ie, selectively choosing and obtaining more data, which can improve the performance of the recommender system. This is done by analyzing the available data and estimating the usefulness of the data points (eg, ratings). [3] In collaborative filtering , elicitation strategies. [4] [5]

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 learned from their respective users. [2]

In recommending 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 based on the ratings assigned by the community to other similar items. Item similarity would be determined according to the items’ content-based characteristics. [1]

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 read the information about a particular music artist from a media portal, then the associated recommender system would automatically suggest that artist’s releases when the user visits the music store. [6]

It is also possible to create a personal profile on a personalized basis. [7] [8] Personality characteristics of the user can be APPROBATION using a model personality Such As five factor model (FFM).

See also

  • Collaborative filtering
  • Preference elicitation
  • Recommended system
  • Active learning (machine learning)
  • Five Factor Model

References

  1. ^ Jump up to:a b Andrew I. Schein; Alexandrin Popescul; Lyle H. Ungar; David M. Pennock (2002). Methods and Metrics for Cold-Start Recommendations. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002). New York City, New York : ACM . pp. 253-260. ISBN  1-58113-561-0 . Retrieved 2008-02-02 .
  2. ^ Jump up to:a b Yezdi Lashkari; Max Metral; Pattie Maes (1994). Collaborative Interface Agents . Proceedings of the Twelfth National Conference on Artificial Intelligence. Seattle , Washington : AAAI Press . pp. 444-449. ISBN  0-262-61102-3 . Retrieved 2008-02-02 .
  3. Jump up^ Rubens, Neil; Elahi, Mehdi; Sugiyama, Masashi; Kaplan, Dain (2016). “Active Learning in Recommend Systems” . In Ricci, Francesco; Rokach, Lior; Shapira, Bracha. Recommend Systems Handbook (2nd ed.). Springer US. ISBN  978-1-4899-7637-6 .
  4. Jump up^ Elahi, Mehdi; Ricci, Francesco; Rubens, Neil. Active Learning in Collaborative Filtering Systems . Springer International Publishing. pp. 113-124. ISBN  978-3-319-10491-1 .
  5. Jump up^ Elahi, Mehdi; Ricci, Francesco; Rubens, Neil (2016). A survey of active learning in collaborative filtering recommenders . Computer Science Review – via Elsevier.
  6. Jump up^ “Vendor attempts to crack” (PDF) . Mobile Media . United Kingdom :Informa Telecoms & Media: 18. 2007-06-29. Archived from the original(PDF) on 2008-08-01 . Retrieved 2008-02-02 .
  7. Jump up^ Tkalcic, Marko; Chen, Li (2016). “Personality and Recommender Systems” . In Ricci, Francesco; Rokach, Lior; Shapira, Bracha. Recommend Systems Handbook (2nd ed.). Springer US. ISBN  978-1-4899-7637-6 .
  8. Jump up^ Fernández-Tobías, Ignacio; Braunhofer, Matthias; Elahi, Mehdi; Ricci, Francesco; Cantador, Iván (2016). “Alleviating the new user problem in collaborative filtering by exploiting personality information” . User Modeling and User-Adapted Interaction . Doi : 10.1007 / s11257-016-9172-z .

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