[u' @inproceedings{sanchez-corcuera_persuade_2019, address = {Leicester, United Kingdom}, title = {Persuade {Me}!: {A} {User}-{Based} {Recommendation} {System} {Approach}}, isbn = {978-1-72814-034-6}, shorttitle = {Persuade {Me}!}, url = {https://ieeexplore.ieee.org/document/9060163/}, doi = {10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00310}, abstract = {Recommendation systems are gaining their momentum with popular Internet platforms such as Amazon, Net\ufb02ix or Spotify. As more users are joining these online consumer and entertainment sectors, the pro\ufb01le-based data for providing accurate just-in-time recommendations is rising thanks to strategies based on collaborative \ufb01ltering or content-based metrics. However, these systems merely focus on providing the right item for the users without taking into account what would be the best strategy to suggest the movie, the product or the song (i.e. the strategy to increase the success or impact of the recommendation). Taking this research gap into consideration, this paper proposes a pro\ufb01le-based recommendation system that outputs a set of potential persuasive strategies that can be used with users with similar characteristics. The scope of the tailored persuasive strategies is to make of\ufb01ce-based employees of tertiary buildings increase their pro-environmental awareness and enhance the energy ef\ufb01ciency at work (the dataset used on this research is speci\ufb01c of this sector). Throughout the paper, shreds of evidence are reported assessing the validity of the proposed system by not only providing effective mechanisms to increase the success of the recommendations but also alleviating the cold-start-problem when newcomers arrive.}, language = {en}, urldate = {2022-05-03}, booktitle = {2019 {IEEE} {SmartWorld}, {Ubiquitous} {Intelligence} \\& {Computing}, {Advanced} \\& {Trusted} {Computing}, {Scalable} {Computing} \\& {Communications}, {Cloud} \\& {Big} {Data} {Computing}, {Internet} of {People} and {Smart} {City} {Innovation} ({SmartWorld}/{SCALCOM}/{UIC}/{ATC}/{CBDCom}/{IOP}/{SCI})}, publisher = {IEEE}, author = {Sanchez-Corcuera, Ruben and Casado-Mansilla, Diego and Borges, Cruz E. and Lopez-De-Ipina, Diego}, month = aug, year = {2019}, keywords = {Feature extraction, Human factors, cold-start problem, collaborative filtering, energy conservation, persuasive strategies, recommendation systems, user profiling, workplace}, pages = {1740--1745}, } ']