Knowledge-based recommender system

Knowledge-based recommender systems (knowledge based recommenders) [1] are a specific type of recommender system that are based on explicit knowledge about the item assortment, user preferences, and recommendation criteria (i.e., which item should be recommended in which context?). These systems are applied in scenarios where alternative approaches such as Collaborative filtering and Content-based filtering cannot be applied. A major strength of knowledge-based recommender systems is the non-existence of cold-start (ramp-up) problems. A corresponding drawback are potential knowledge acquisition bottlenecks triggered by the need of defining recommendation knowledge in an explicit fashion.

Item domains

Items such as apartments and cars are not purchased very often, therefore rating-based systems often do not perform well due to a low number of available ratings.[1] In complex item domains customers want to specify their preferences explicitly (e.g., "the maximum price of the car is X") . In this context, constraints have to be taken into account by the recommender system, for example, only financial services must be recommended that support the investment period specified by the customer. Both latter aspects are not supported by approaches such as Collaborative filtering and Content-based filtering. Further examples of item domains relevant for knowledge-based recommender systems are financial services,[2] digital cameras,[3] and tourist destinations.[4]

Conversational recommendation

Knowledge-based recommender systems are often conversational, i.e., user requirements and preferences are elicited within the scope of a feedback loop. A major reason for the conversational nature of knowledge-based recommender systems is the complexity of the item domain where it is often impossible to articulate all user preferences at once. Furthermore, user preferences are typically not known exactly at the beginning but are constructed within the scope of a recommendation session.[5]

Search-based recommendation

In a search-based recommender, user feedback is given in terms of answers to questions which restrict the set of relevant items.[6] An example of such a question is "Which type of lens system do you prefer: fixed or exchangeable lenses?". On the technical level, search-based recommendation scenarios can be implemented on the basis of constraint-based recommender systems.[6] Constraint-based recommender systems are implemented on the basis of constraint search [6][7] or different types of conjunctive query-based approaches.[8]

Navigation-based recommendation

In a navigation-based recommender, user feedback is typically provided in terms of "critiques" [9] which specify change requests regarding the item currently recommended to the user. Critiques are then used for the recommendation of the next "candidate" item. An example of a critique in the context of a digital camera recommendation scenario is "I would like to have a camera like this but with a lower price". This is an example of a "unit critique" [1] which represents a change request on a single item attribute. "Compound critiques" [3] allow the specification of more than one change request at a time. "Dynamic critiquing" [10] also takes into account preceding user critiques (the critiquing history). More recent approaches additionally exploit information stored in user interaction logs to further reduce the interaction effort in terms of the number of needed critiquing cycles.[11][12][13][14] [15]

See also

References

  1. ↑ 1.0 1.1 1.2 R. Burke, Knowledge-based Recommender Systems, Encyclopedia of Library and Information Science, 69(32):180-200, 2000.
  2. ↑ A. Felfernig, K. Isak, K. Szabo, and P. Zachar, The VITA Financial Services Sales Support Environment, AAAI/IAAI 2007, pp. 1692-1699, Vancouver, Canada, 2007.
  3. ↑ 3.0 3.1 K. McCarthy, R. Reilly, B. Smyth, and L. McGinty, Generating diverse compound critiques, Artificial Intelligence Review 24(3-4):339-357, 2005.
  4. ↑ F. Ricci and Q. Nguyen, Acquiring and revising preferences in a critiquing-based mobile recommender system, IEEE Intelligent Systems 22(3):22-29, 2007.
  5. ↑ L. Chen, M.deGemmis, A. Felfernig, P. Lops, F. Ricci, and G. Semeraro. Human Decision Making and Recommender Systems, ACM Transactions on Interactive Intelligent Systems, 3(3):17, 2013.
  6. ↑ 6.0 6.1 6.2 A. Felfernig and R. Burke, Constraint-based Recommender Systems: Technologies and Research Issues, ACM International Conference on Electronic Commerce (ICEC'08), pp. 17-26, 2008.
  7. ↑ A. Mackworth. Consistency in networks of relations, Artificial Intelligence, 8(1):99-118, 1977.
  8. ↑ A. Felfernig, S. Reiterer, M. Stettinger, and M. Jeran. An Overview of Direct Diagnosis and Repair Techniques in the WeeVis Recommendation Environment, International Workshop on Principles of Diagnosis, pp. 1-6, 2014.
  9. ↑ L. Chen and P. Pu. Critiquing-based recommenders: survey and emerging trends, User Modeling and User-Adapted Interaction Journal (UMUAI), 22(1-2):125-150, 2012.
  10. ↑ J.Reilly, K. McCarthy, L. McGinty, and B. Smyth. Dynamic Critiquing, ECCBR 2004, pp. 763-777, 2004.
  11. ↑ K. McCarthy, Y.Salem, and B. Smyth. Experience-Based Critiquing: Reusing Critiquing Experiences to Improve Conversational Recommendation, ICCBR'10, pp. 480-494, 2010.
  12. ↑ M.Mandl and A. Felfernig. Improving the Performance of Unit Critiquing, 20th International Conference on User Modeling, Adaptation, and Personalization (UMAP 2012), pp. 176-187, Montreal, Canada, 2012.
  13. ↑ Y.Salem, and J. Hong. History-aware critiquing-based conversational recommendation, World Wide Web (WWW 2013), pp. 63-64, 2013.
  14. ↑ Y.Salem, J. Hong, and W. Liu. History-Guided Conversational Recommendation, World Wide Web (WWW 2014), pp. 999-1004, 2014.
  15. ↑ H. Xie, L.Chen, and F. Wang. Collaborative Compound Critiquing, UMAP 2014, pp. 254-265, 2014.

External links

Systems and datasets
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