ACM Transactions on Information Systems

Công bố khoa học tiêu biểu

* Dữ liệu chỉ mang tính chất tham khảo

Sắp xếp:  
Technological frames
ACM Transactions on Information Systems - Tập 12 Số 2 - Trang 174-207 - 1994
Wanda J. Orlikowski, Debra C. Gash
In this article, we build on and extend research into the cognitions and values of users and designers by proposing a systematic approach for examining the underlying assumptions, expectations, and knowledge that people have about technology. Such interpretations of technology (which we call technological frames) are central to understanding technological development, use, and change in or...... hiện toàn bộ
Cross-Platform App Recommendation by Jointly Modeling Ratings and Texts
ACM Transactions on Information Systems - Tập 35 Số 4 - Trang 1-27 - 2017
Da Cao, Xiangnan He, Liqiang Nie, Xiaochi Wei, Xia Hu, Shunxiang Wu, Tat‐Seng Chua
Over the last decade, the renaissance of Web technologies has transformed the online world into an application (App) driven society. While the abundant Apps have provided great convenience, their sheer number also leads to severe information overload, making it difficult for users to identify desired Apps. To alleviate the information overloading issue, recommender systems have been propos...... hiện toàn bộ
PageRank
ACM Transactions on Information Systems - Tập 27 Số 4 - Trang 1-23 - 2009
Paolo Boldi, Massimo Santini, Sebastiano Vigna
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems - Tập 12 Số 3 - Trang 252-277 - 1994
Yiming Yang, Christopher G. Chute
A unified model for text categorization and text retrieval is introduced. We use a training set of manually categorized documents to learn word-category associations, and use these associations to predict the categories of arbitrary documents. Similarly, we use a training set of queries and their related documents to obtain empirical associations between query words and indexing terms of d...... hiện toàn bộ
PocketLens
ACM Transactions on Information Systems - Tập 22 Số 3 - Trang 437-476 - 2004
Brad Miller, Joseph A. Konstan, John Riedl
Recommender systems using collaborative filtering are a popular technique for reducing information overload and finding products to purchase. One limitation of current recommenders is that they are not portable. They can only run on large computers connected to the Internet. A second limitation is that they require the user to trust the owner of the recommender with personal preference dat...... hiện toàn bộ
gIBIS: a hypertext tool for exploratory policy discussion
ACM Transactions on Information Systems - Tập 6 Số 4 - Trang 303-331 - 1988
Jeff Conklin, Michael L. Begeman
This paper describes an application-specific hypertext system designed to facilitate the capture of early design deliberations. It implements a specific method, called Issue Based Information Systems (IBIS), which has been developed for use on large, complex design problems. The hypertext system described here, gIBIS (for graphical IBIS), makes use of color and a high-speed relational data...... hiện toàn bộ
Extending object-oriented systems with roles
ACM Transactions on Information Systems - Tập 14 Số 3 - Trang 268-296 - 1996
Georg Gottlob, Michael Schrefl, Brigitte Röck
In many class-based object-oriented systems the association between as instance and a class is exclusive and permanent. Therefore these systems have serious difficulties in representing objects taking on different roles over time. Such objects must be reclassified any time they evolve (e.g., if a person becomes a student and later an employee). Class hierarchies must be planned carefully a...... hiện toàn bộ
Stability of Recommendation Algorithms
ACM Transactions on Information Systems - Tập 30 Số 4 - Trang 1-31 - 2012
Gediminas Adomavičius, Jingjing Zhang
The article explores stability as a new measure of recommender systems performance. Stability is defined to measure the extent to which a recommendation algorithm provides predictions that are consistent with each other. Specifically, for a stable algorithm, adding some of the algorithm’s own predictions to the algorithm’s training data (for example, if these predictions were confirmed as ...... hiện toàn bộ
Joint Neural Collaborative Filtering for Recommender Systems
ACM Transactions on Information Systems - Tập 37 Số 4 - Trang 1-30 - 2019
Wanyu Chen, Fei Cai, Honghui Chen, Maarten de Rijke
We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modelin...... hiện toàn bộ
Deep Item-based Collaborative Filtering for Top-N Recommendation
ACM Transactions on Information Systems - Tập 37 Số 3 - Trang 1-25 - 2019
Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong
Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user’s profile with the items that the user has consumed, ICF recommends items that are similar to the user’s profile. With the prevalenc...... hiện toàn bộ
Tổng số: 22   
  • 1
  • 2
  • 3