Preference relation-based markov random fields

A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and signifi cantly improved Top-N recommendation performance has been achieved.

Preference relation-based Markov Random Fields for recommender systems

A preference relation-based Top-N recommendation approach is proposed to capture both second-order and higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings first, and then inferring the item rankings, based on the assumption of availability of explicit feedback such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed approach drops these assumptions by exploiting preference relations, a more practical user feedback. Furthermore, the proposed approach enjoys the representational power of Markov Random Fields thus side information such as item and user attributes can be easily incorporated. Comparing to related work, the proposed approach has the unique property of modeling both second-order and higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference-relation based methods. Experimental results on public datasets demonstrate that both types of interactions have been properly captured, and significantly improved Top-N recommendation performance has been achieved.

Construction and aggregation of preference relations based on fuzzy partial orders

In group decision-making problems it is common to elicit preferences from human experts in the form of pairwise preference relations. When this is extended to a fuzzy setting, entries in the pairwise preference matrix are interpreted to denote strength of preference, however once logical properties such as consistency and transitivity are enforced, the resulting preference relation requires almost as much information as providing raw scores or a complete order over the alternatives. Here we instead interpret fuzzy degrees of preference to only apply where the preference over two alternatives is genuinely fuzzy and then suggest an aggregation procedure that minimizes a generalized Kemeny distance to the nearest complete or partial order. By focusing on the fuzzy partial order, the method is less affected by differences in the natural scale over which an expert expresses their preference, and can also limit the influence of extreme scores.

Relative preference-based recommender systems

This study investigates the problem of making recommendations to users, such as recommending a movie. Several novel models are proposed to make accurate recommendations by analyzing both the explicit and implicit data. Experiment results have confirmed improvements over state-of-the-art models.

Penalty functions over a cartesian product of lattices

In this work we present the concept of penalty function over a Cartesian product of lattices. To build these mappings, we make use of restricted dissimilarity functions and distances between fuzzy sets. We also present an algorithm that extends the weighted voting method for a fuzzy preference relation.

Play preference of children with ADHD and typically developing children in Brazil : a pilot study

Background/aim: Children with attention deficit hyperactivity disorder (ADHD) have been reported to have play deficits, which can cause problems in occupational development. The aim of this paper was to report research findings on children with ADHD and typically developing children in relation to preference of play partners, play places, toys and type of play.

Methods: Thirty-two school aged children from lowsocioeconomic status were divided into two groups. One group of 16 children with ADHD were matched with 16 typically developing children.

Results and conclusion: There were no significant differences between the two groups in relation to play partners, with classmates being the most frequent play partner for both groups. There were significant differences between the two groups in preferred place to play. Children with ADHD preferred to play in school and typically developing children preferred to play on the street. There were significant differences in relation to toys and type of play engaged in with children with ADHD preferring educational materials and typically developing children preferring electronic games. These findings add to knowledge of Brazilian children with ADHD and their play preferences. Comparisons are made with research with Australian children with and without ADHD.