Personalized Recommendation Model Based on Quantifier Induced by Preference

A novel model for extracting expected value from users is presented, with which to establish a user preference-based personalized quantifier. A sample of multi-attribute alternatives is given first, and then the involved user is asked to provide a ranking of alternatives of this sample on the basis of his/her personal preference or decision attitude. With this ranking, an extraction model for users’ expected value about the sample information is constructed. The principles of OWA (ordered weighted averaging) aggregations and TOPSIS (technique for order preference by similarity to ideal solution) are followed during the modeling, upon which the developed technique is based. The user’s preference or attitude can then be derived from this expected value to help build a personalized quantifier, with which to aggregate the attribute values of new products with the aim of realizing personalized recommendation. Case study and experimental results show that the developed model and quantifier can well capture and reflect many varieties of personality characteristics of users with different ability levels and knowledge structures. As such, the developed technique could be considered as an effective tool in practical applications for the “satisfactory solutions” in accord with some particular attitude, rather than the “optimal solutions” in general terms, characterized by greater applicability and flexibility by contrast with a similar kind of method.