Search

Your search keyword '"Hu, Zhongli"' showing total 478 results
Start Over Author "Hu, Zhongli"
478 results on '"Hu, Zhongli"'

3. Justification of Recommender Systems Results: A Service-based Approach

Author

Mauro, Noemi, Hu, Zhongli Filippo, and Ardissono, Liliana

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction
Abstract

With the increasing demand for predictable and accountable Artificial Intelligence, the ability to explain or justify recommender systems results by specifying how items are suggested, or why they are relevant, has become a primary goal. However, current models do not explicitly represent the services and actors that the user might encounter during the overall interaction with an item, from its selection to its usage. Thus, they cannot assess their impact on the user's experience. To address this issue, we propose a novel justification approach that uses service models to (i) extract experience data from reviews concerning all the stages of interaction with items, at different granularity levels, and (ii) organize the justification of recommendations around those stages. In a user study, we compared our approach with baselines reflecting the state of the art in the justification of recommender systems results. The participants evaluated the Perceived User Awareness Support provided by our service-based justification models higher than the one offered by the baselines. Moreover, our models received higher Interface Adequacy and Satisfaction evaluations by users having different levels of Curiosity or low Need for Cognition (NfC). Differently, high NfC participants preferred a direct inspection of item reviews. These findings encourage the adoption of service models to justify recommender systems results but suggest the investigation of personalization strategies to suit diverse interaction needs.

Published
2022
Full Text
View/download PDF

9. Multi-faceted Trust-based Collaborative Filtering

Author

Mauro, Noemi, Ardissono, Liliana, and Hu, Zhongli Filippo

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that can contribute to determine users' global reputation; e.g., public recognition of reviewers' quality. We are interested in understanding if and when these additional types of feedback improve Top-N recommendation. For this purpose, we propose a multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. We aim at identifying general classes of data in order to make our model applicable to different case studies. Then, we test the model by applying it to a variant of User-to-User Collaborative filtering (U2UCF) which supports the fusion of rating similarity, local trust derived from social relations, and multi-faceted reputation for rating prediction. We test our model on two datasets: the Yelp one publishes generic friend relations between users but provides different types of trust feedback, including user profile endorsements. The LibraryThing dataset offers fewer types of feedback but it provides more selective friend relations aimed at content sharing. The results of our experiments show that, on the Yelp dataset, our model outperforms both U2UCF and state-of-the-art trust-based recommenders that only use rating similarity and social relations. Differently, in the LibraryThing dataset, the combination of social relations and rating similarity achieves the best results. The lesson we learn is that multi-faceted trust can be a valuable type of information for recommendation. However, before using it in an application domain, an analysis of the type and amount of available trust evidence has to be done to assess its real impact on recommendation performance.

Published
2020
Full Text
View/download PDF

13. Research on CAT into MTI Teaching in GDUFS

Author

Du, Weihua, Wang, Meiling, Hu, Zhongli, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Holl, Augustin, editor, Chen, Jun, editor, and Guan, Guiyun, editor

Published
2022
Full Text
View/download PDF

16. A Study on the Attitude and Practice of Chinese Teachers and Students Towards VR Teaching for Foreign Language Learning

Author

Du, Weihua, Hu, Zhongli, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Russo, Dario, editor, Ahram, Tareq, editor, Karwowski, Waldemar, editor, Di Bucchianico, Giuseppe, editor, and Taiar, Redha, editor

Published
2021
Full Text
View/download PDF

17. Service-Oriented Justification of Recommender System Suggestions

Author

Mauro, Noemi, Hu, Zhongli Filippo, Ardissono, Liliana, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ardito, Carmelo, editor, Lanzilotti, Rosa, editor, Malizia, Alessio, editor, Petrie, Helen, editor, Piccinno, Antonio, editor, Desolda, Giuseppe, editor, and Inkpen, Kori, editor

Published
2021
Full Text
View/download PDF

18. Organic Anodes with Nearly 100% Initial Coulombic Efficiency Enabled by Wet‐Chemically Constructed Artificial Solid‐Electrolyte Interphase Film toward High‐Energy‐Density Organic Full Batteries

Author

Hu, Zhongli, primary, Liu, Junjie, additional, Zhao, Xiaolin, additional, Zhan, Xiao, additional, Wang, Huiqun, additional, Yang, Huiping, additional, Wu, Jiedu, additional, Fang, Xiaoliang, additional, Zhang, Qiaobao, additional, Liu, Jianjun, additional, and Zhang, Li, additional

Published
2024
Full Text
View/download PDF

40. High embryogenic ability and regeneration from floral axis of Amorphophallus konjac (Araceae)

Author

Zhong Lin, Liu Erxi, Yang Chaozhu, Jin Surong, Diao Ying, and Hu Zhongli

Subjects
amorphophallus konjac, embryogenic callus, high reproduction rate, in vitro production, tissue culture, Biology (General), QH301-705.5
Abstract

Amorphophallus konjac (Araceae) a perennial herb, it has high medicinal and industrial value. In this study, a simple and efficient system for direct somatic embryogenesis and plantlet regeneration of Amorphophallus konjac was developed. The floral axis was used as the experimental material. The primary callus, developed from the floral axis grown on Murashige and Skoog (MS) medium supplemented with different hormone combination at different concentrations. The highest rate of embryogenic callus formation was observed on the MS medium containing 9.04 µM 2, 4-dichlorophenoxyacetic acid (2, 4-D) and 5.37 µM naphthalene acetic acid (NAA). The maximum induction rate was 79.8%, and the embryogenic calli were able to subculture on a medium containing similar hormone combination for over 1 year. The calli were also placed on different media for regeneration and it produced complete plants with shoots and root systems simultaneously. The highest differentiation rate of the embryogenic calli grown on differentiation medium supplemented with 8.88 µM 6-benzylaminopurine (6-BA) and 5.37 µM NAA was 95.6%. Flow cytometry analysis showed no ploidy variation in all the regenerate plantlets.

Published
2017
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results