308,271 research outputs found
A Framework for paper submission recommendation system
Nowadays, recommendation systems play an indispensable role in
many fields, including e-commerce, finance, economy, and gaming.
There is emerging research on publication venue recommendation
systems to support researchers when submitting their scientific
work. Several publishers such as IEEE, Springer, and Elsevier have
implemented their submission recommendation systems only to
help researchers choose appropriate conferences or journals for submission. In this work, we present a demo framework to construct an
effective recommendation system for paper submission. With the
input data (the title, the abstract, and the list of possible keywords)
of a given manuscript, the system recommends the list of top relevant journals or conferences to authors. By using state-of-the-art
techniques in natural language understanding, we combine the features extracted with other useful handcrafted features. We utilize
deep learning models to build an efficient recommendation engine
for the proposed system. Finally, we present the User Interface
(UI) and the architecture of our paper submission recommendation
system for later usage by researchers
A review of performance measurement: towards performance management
ReviewCopyright © 2005 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Computers in Industry. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers in Industry (2005), DOI: 10.1016/j.compind.2005.03.001Describes the evolution of performance measurement (PM) in four sections: recommendations, frameworks, systems and inter-organisational performance measurement. Measurement begins with a recommendation, which is a piece of advice related to the measures or structure of performance measurement; frameworks can be dichotomised into a structural and procedural typology that suggests structural framework development has outstripped procedural framework development. The basic requirements for a successful PM system are two frameworks—one structural and one procedural as well as a number of other performance management tools. Inter-organisational performance measurement may be divided into supply chain and extended enterprise performance measurement: the former relying solely on traditional logistics measures, while the latter incorporates the structural aspects of the supply chain system and adds non-logistics perspectives to its measurement arena. Finally, the encroachment of the performance measurement literature into the processes related to performance management is examined, and areas for future research are suggested
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Static recommendation methods like collaborative filtering suffer from the
inherent limitation of performing real-time personalization for cold-start
users. Online recommendation, e.g., multi-armed bandit approach, addresses this
limitation by interactively exploring user preference online and pursuing the
exploration-exploitation (EE) trade-off. However, existing bandit-based methods
model recommendation actions homogeneously. Specifically, they only consider
the items as the arms, being incapable of handling the item attributes, which
naturally provide interpretable information of user's current demands and can
effectively filter out undesired items. In this work, we consider the
conversational recommendation for cold-start users, where a system can both ask
the attributes from and recommend items to a user interactively. This important
scenario was studied in a recent work. However, it employs a hand-crafted
function to decide when to ask attributes or make recommendations. Such
separate modeling of attributes and items makes the effectiveness of the system
highly rely on the choice of the hand-crafted function, thus introducing
fragility to the system. To address this limitation, we seamlessly unify
attributes and items in the same arm space and achieve their EE trade-offs
automatically using the framework of Thompson Sampling. Our Conversational
Thompson Sampling (ConTS) model holistically solves all questions in
conversational recommendation by choosing the arm with the maximal reward to
play. Extensive experiments on three benchmark datasets show that ConTS
outperforms the state-of-the-art methods Conversational UCB (ConUCB) and
Estimation-Action-Reflection model in both metrics of success rate and average
number of conversation turns.Comment: TOIS 202
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