Supporting Online Data Purchase by Preference Recommendation
Denis Mayr Lima Martins, Gottfried Vossen, Marcin Maleszka · 2018
With the increasing availability and production of data, data-driven decision-making has been largely adopted by people and organizations. However, finding and purchasing data suitable for a specific purpose and addressing unique (and sometimes) personal requirements is still a challenge. Alternative solutions to the traditional data commerce, the data marketplaces, are of almost no help for novice buyers, as the number of datasets that are available to purchase is often massive. To alleviate this problem, we consider an intelligent decision support approach for helping buyers, particularly inexperienced ones, to better search and evaluate data offerings. The contribution of this work consists of a preference recommendation strategy that focuses a buyer's attention towards more appropriate offers while enhancing his or her awareness of the market. An experimental case is designed to show the feasibility and effectiveness of the approach. The results obtained demonstrate that preference recommendation has the potential to suggest unexpected, yet useful data offerings to buyers.