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• W2561618650 abstract "Recommender systems are quickly becoming ubiquitous in applications such as e-commerce, social media channels, content providers, among others, acting as an enabling mechanism designed to overcome the information overload problem by improving browsing and consumption experience. A typical task in many recommender systems is to output a ranked list of items, so that items placed higher in the rank are more likely to be interesting to the users. Interestingness measures include how accurate, novel and diverse are the suggested items, and the objective is usually to produce ranked lists optimizing one of these measures. Suggesting items that are simultaneously accurate, novel and diverse is much more challenging, since this may lead to a conflicting-objective problem, in which the attempt to improve a measure further may result in worsening other measures. In this paper we propose new approaches for multi-objective recommender systems based on the concept of Pareto-efficiency − a state achieved when the system is devised in the most efficient manner in the sense that there is no way to improve one of the objectives without making any other objective worse off. Given that existing multi-objective recommendation algorithms differ in their level of accuracy, diversity and novelty, we exploit the Pareto-efficiency concept in two distinct manners: (i) the aggregation of ranked lists produced by existing algorithms into a single one, which we call Paretoefficient ranking, and (ii) the weighted combination of existing algorithms resulting in a hybrid one, which we call Pareto-efficient hybridization. Our evaluation involves two real application scenarios: music recommendation with implicit feedback (i.e., Last.fm) and movie recommendation with explicit feedback (i.e., MovieLens). We show that the proposed Pareto-efficient approaches are effective in suggesting items that are likely to be simultaneously accurate, diverse and novel. We discuss scenarios where the system achieves high levels of diversity and novelty without compromising its accuracy. Further, comparison against multiobjective baselines reveals improvements in terms of accuracy (from 10.4% to 10.9%), novelty (from 5.7% to 7.5%), and diversity(from 1.6% to 4.2%)." @default.
• W2561618650 created "2017-01-06" @default.
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• W2561618650 date "2013-01-01" @default.
• W2561618650 modified "2023-09-22" @default.
• W2561618650 title "Multi-Objective Pareto-Efficient Approaches for Recommend er Systems" @default.
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