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Engineering Microbial Particles for Next‐Generation Biomedical Platforms

Advanced Science, EarlyView.
Microbe‐derived particles (MDPs), which include extracellular vesicles, outer membrane vesicles, inclusion bodies, polysaccharide particles, and virus‐like particles, represent a rapidly expanding category of bioinspired nanomaterials. With their natural origin, intrinsic biocompatibility, and highly programmable functionality, MDPs serve as a ...
Yuting Li, Yunjie Lu, Ziyan Huang, Jianjiang Chen, Yanbin Liu, Lei Qin, Chao Wang, Yumin Wu   +7 more
wiley   +1 more source

A multi-level collaborative filtering method that improves recommendations

Expert Systems With Applications, 2016
Collaborative filtering is one of the most used approaches for providingrecommendations in various online environments. Even though collaborativerecommendation methods have been widely utilized due to their simplicity andease of use, accuracy is still an
Nikolaos Polatidis, Christos K Georgiadis   +1 more
exaly   +2 more sources

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Listwise Collaborative Filtering

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015
Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do).
Huang, Shanshan, Wang, Shuaiqiang, Liu, Tie-Yan, Ma, Jun, Chen, Zhumin, Veijalainen, Jari   +6 more
openaire   +2 more sources

Adaptive collaborative filtering

Proceedings of the 2008 ACM conference on Recommender systems, 2008
We present a flexible approach to collaborative filtering which stems from basic research results. The approach is flexible in several dimensions: We introduce an algorithm where the loss can be tailored to a particular recommender problem. This allows us to optimize the prediction quality in a way that matters for the specific recommender system.
Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola   +2 more
openaire   +1 more source

Collaborative Filtering with CCAM

2011 10th International Conference on Machine Learning and Applications and Workshops, 2011
Recommender system has become an important research topic since the high interest of academia and industry. As a branch of recommender systems, collaborative filtering (CF) systems take its roots from sharing opinions with others and have been shown to be very effective for generating high quality recommendations.
Meng-Lun Wu, Chia-Hui Chang, Rui-Zhe Liu
openaire   +1 more source

Shared collaborative filtering

Proceedings of the fifth ACM conference on Recommender systems, 2011
Traditional collaborative filtering (CF) methods suffer from sparse or even cold-start problems, especially for new established recommenders. However, since there are now quite a few recommender systems already existing in good working order, their data should be valuable to the new-start recommenders. This paper proposes shared collaborative filtering
Yu Zhao 0002, Xinping Feng, Jianqiang Li 0002, Bo Liu 0010   +3 more
openaire   +1 more source

Discrete Collaborative Filtering

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016
We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a Hamming space. However, existing hashing methods for CF employ binary code learning procedures that most suffer from the challenging discrete ...
Hanwang Zhang, Fumin Shen, Wei Liu 0005, Xiangnan He 0001, Huanbo Luan, Tat-Seng Chua   +5 more
openaire   +1 more source

Improved Collaborative Filtering

2011
We consider the interactive model of collaborative filtering, where each member of a given set of users has a grade for each object in a given set of objects. The users do not know the grades at start, but a user can probe any object, thereby learning her grade for that object directly.
Aviv Nisgav, Boaz Patt-Shamir
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Agents for Collaborative Filtering

2003
This paper describes a new generic agent-based framework for collaborative filtering. Usually, collaborative filtering tools use large collaborative document databases to model users’ preferences. Nevertheless, we believe that collaborative filtering can be accomplished with decentralized systems in which user’s preferences are learned from small ...
Fabrício Enembreck, Jean-Paul A. Barthès   +1 more
openaire   +1 more source

Community Collaborative Filtering

2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008
This paper presents a novel approach from a perspective of considering community structures to collaborative filtering. In our approach, multiple types of information are be explored and exploited, including item content, user demography, use-item ratings, use-item structure and user social information.
openaire   +1 more source