Most existing task scheduling algorithms in cloud storage fail to aware users ' QoS preference. In addition, these algorithms result in low user satisfaction rate for they do not consider the characteristics of cloud storage. In order to address these problems, the "optimal order comparison method” is used to aware users ' QoS preference, and also helps experts use their professional knowledge ...

Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neigh...

This paper pursues the idea of a general multiobjective optimizer that can be flexibly adapted to arbitrary user preferences— assuming that the goal is to approximate the Pareto-optimal set. It proposes the Set Preference Algorithm for Multiobjective Optimization (SPAM) the working principle of which is based on two observations: (i) current multiobjective evolutionary algorithms (MOEAs) can be...

Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Non-collaborative user modeling techniques that rely on the item features can be used to recommend new items. However, they only use the past preferences of each user to provide recommendations for that user. They do not utilize information from the past pre...

Point-of-interest (POI) recommendation has become more and more important, since it could discover user behavior pattern and find interesting venues for them. To address this problem, we propose a rank-based method, PGRank, which integrates user geographical preference and latent preference into Bayesian personalized ranking framework. The experimental results on a real dataset show its effective.

In this paper, we study the problem of retrieving a ranked list of top-N items to a target user in recommender systems. We first develop a novel preference model by distinguishing different rating patterns of users, and then apply it to existing collaborative filtering (CF) algorithms. Our preference model, which is inspired by a voting method, is well-suited for representing qualitative user p...

This research concentrates on designing generic product-brokering agent to understand user preference towards a product category and recommends a list of products to the user according to the preference captured by the agent. The proposed solution is able to detect both quantifiable and non-quantifiable attributes through a user feedback system. Unlike previous approaches, this research allows ...

Preference representation and reasoning play a central role in supporting users with complex and multi-factorial decision processes. In fact, user tastes can be used to filter information and data in a personalized way, thus maximizing their expected utility. Over the years, many frameworks and languages have been proposed to deal with user preferences. Among them, one of the most prominent for...

In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for many-objective problems. In a user-preference based algorithm a decision maker indicates regions of the objective-space of interest, the algorithm then concentrates only on those regions to find solutions. Existing user-preference based evolutionary many-objective algorithms rely on the...

As information overload problem more serious on the Internet, it becomes an important issue for users to retrieve information effectively. An information recommendation system is helpful for providing user information meet he/she requirements appropriately. However the traditional recommendation concepts usual classify a user into one preference class. It seems unreasonable because a user may p...