ACM Transactions on Information Systems
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Technological frames
ACM Transactions on Information Systems - Tập 12 Số 2 - Trang 174-207 - 1994
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 organizati...
Cross-Platform App Recommendation by Jointly Modeling Ratings and Texts
ACM Transactions on Information Systems - Tập 35 Số 4 - Trang 1-27 - 2017
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
proposed and d...
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems - Tập 12 Số 3 - Trang 252-277 - 1994
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 documents...
PocketLens
ACM Transactions on Information Systems - Tập 22 Số 3 - Trang 437-476 - 2004
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
data. Perso...
gIBIS: a hypertext tool for exploratory policy discussion
ACM Transactions on Information Systems - Tập 6 Số 4 - Trang 303-331 - 1988
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
database ser...
Extending object-oriented systems with roles
ACM Transactions on Information Systems - Tập 14 Số 3 - Trang 268-296 - 1996
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 and
may g...
Stability of Recommendation Algorithms
ACM Transactions on Information Systems - Tập 30 Số 4 - Trang 1-31 - 2012
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 accurate...
Joint Neural Collaborative Filtering for Recommender Systems
ACM Transactions on Information Systems - Tập 37 Số 4 - Trang 1-30 - 2019
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
modeling captur...
Deep Item-based Collaborative Filtering for Top-N Recommendation
ACM Transactions on Information Systems - Tập 37 Số 3 - Trang 1-25 - 2019
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 prevalence of machine learning in recent years, significant processes...
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