Basis Functions as Pivots in Space of Users Preferences
Our starting motivation is a user visiting an e-shop. E-shops usually offer conjunction of sharp filter conditions and one attribute ordering of results. We use a top-k query system where results are ordered by a multi-criterial monotone combination of soft filter conditions.
For prediction of users’ behavior, we introduce a class of basis functions with positive Linear combination of Triangular (soft) filters (LT). We prove that LT gives a unique representation of preferences. From database point of view LT act as a source for choosing pivots. From business perspective LT reflect aggregation of users’ (soft) ideal values (choice points).
Our experiments use artificial data and are organized along variants of user’s search habits, learning algorithms and evaluation measures. We argue that LT recommendations behave better with respect to order sensitive measures. This gives raise a problem of pivot based indexing with order sensitive metrics.
KeywordsBusiness intelligence and analytics Recommender systems User preference learning Pivot based indexing Experiments Evaluation measures
We announce partial support of Czech grants P103-15-19877S, 16-09103S and P46.
- 3.Peska, L., Vojtas, P.: Using implicit preference relations to improve recommender systems. J. Data Semant., 1–16 (2016). http://dx.doi.org/10.1007/s13740-016-0061-8
- 4.Vojtas, P.: Linear combinations of triangular attribute preferences provide unique representation of user preferences. Seminar of Prof. E. Huellermeier, Marburg, 26 February 2013Google Scholar
- 5.Vojtas, P., Kopecky, M., Vomlelova, M.: Understanding transparent and complicated users as instances of preference learning for recommender systems. In: Kofron, J., Vojnar, T. (eds.) MEMICS 2015. LNCS, vol. 9548, pp. 23–34. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-29817-7_3 CrossRefGoogle Scholar