23,090 research outputs found
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approaches mainly use the trust scores explicitly expressed by
users. However, it is often challenging to have users provide explicit trust
scores of each other. There exist quite a few works, which propose Trust
Metrics to compute and predict trust scores between users based on their
interactions. In this paper, first we present how social relation can be
extracted from users' ratings to items by describing Hellinger distance between
users in recommender systems. Then, we propose to incorporate the predicted
trust scores into social matrix factorization models. By analyzing social
relation extraction from three well-known real-world datasets, which both:
trust and recommendation data available, we conclude that using the implicit
social relation in social recommendation techniques has almost the same
performance compared to the actual trust scores explicitly expressed by users.
Hence, we build our method, called Hell-TrustSVD, on top of the
state-of-the-art social recommendation technique to incorporate both the
extracted implicit social relations and ratings given by users on the
prediction of items for an active user. To the best of our knowledge, this is
the first work to extend TrustSVD with extracted social trust information. The
experimental results support the idea of employing implicit trust into matrix
factorization whenever explicit trust is not available, can perform much better
than the state-of-the-art approaches in user rating prediction
Understanding the Importance and Impact of Technology in an Accounting Setting: Work Outcomes and Relationships with Clients
This study explores how technology positively or negatively impacts the accounting profession, and specifically, the impact on work outcomes (i.e. the effectiveness and efficiency of work) and relationships with clients. Three types of technology tools were featured in this study: Accounting and Analytics, Robotic Process Automation, and Communication Technology Tools and Platforms. Our research questions were (1) How much do technology tools improve the efficiency and effectiveness of the accountant? and (2) How much do technology tools affect the relationship with clients? After surveying professionals in the accounting field, we concluded that accountants believe that Communication softwares improve their efficiency and effectiveness the most, with Accounting and Analytics softwares just behind. We can also conclude that technology has a positive, or at the very least, neutral, effect on the relationship between professionals and their clients. Overall, it was found that in the accounting field, technology has a positive impact on work outcomes and relationships with clients
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
For a company looking to provide delightful user experiences, it is of
paramount importance to take care of any customer issues. This paper proposes
COTA, a system to improve speed and reliability of customer support for end
users through automated ticket classification and answers selection for support
representatives. Two machine learning and natural language processing
techniques are demonstrated: one relying on feature engineering (COTA v1) and
the other exploiting raw signals through deep learning architectures (COTA v2).
COTA v1 employs a new approach that converts the multi-classification task into
a ranking problem, demonstrating significantly better performance in the case
of thousands of classes. For COTA v2, we propose an Encoder-Combiner-Decoder, a
novel deep learning architecture that allows for heterogeneous input and output
feature types and injection of prior knowledge through network architecture
choices. This paper compares these models and their variants on the task of
ticket classification and answer selection, showing model COTA v2 outperforms
COTA v1, and analyzes their inner workings and shortcomings. Finally, an A/B
test is conducted in a production setting validating the real-world impact of
COTA in reducing issue resolution time by 10 percent without reducing customer
satisfaction
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction
As algorithms are increasingly used to make important decisions that affect
human lives, ranging from social benefit assignment to predicting risk of
criminal recidivism, concerns have been raised about the fairness of
algorithmic decision making. Most prior works on algorithmic fairness
normatively prescribe how fair decisions ought to be made. In contrast, here,
we descriptively survey users for how they perceive and reason about fairness
in algorithmic decision making.
A key contribution of this work is the framework we propose to understand why
people perceive certain features as fair or unfair to be used in algorithms.
Our framework identifies eight properties of features, such as relevance,
volitionality and reliability, as latent considerations that inform people's
moral judgments about the fairness of feature use in decision-making
algorithms. We validate our framework through a series of scenario-based
surveys with 576 people. We find that, based on a person's assessment of the
eight latent properties of a feature in our exemplar scenario, we can
accurately (> 85%) predict if the person will judge the use of the feature as
fair.
Our findings have important implications. At a high-level, we show that
people's unfairness concerns are multi-dimensional and argue that future
studies need to address unfairness concerns beyond discrimination. At a
low-level, we find considerable disagreements in people's fairness judgments.
We identify root causes of the disagreements, and note possible pathways to
resolve them.Comment: To appear in the Proceedings of the Web Conference (WWW 2018). Code
available at https://fate-computing.mpi-sws.org/procedural_fairness
Weighted Random Walk Sampling for Multi-Relational Recommendation
In the information overloaded web, personalized recommender systems are
essential tools to help users find most relevant information. The most
heavily-used recommendation frameworks assume user interactions that are
characterized by a single relation. However, for many tasks, such as
recommendation in social networks, user-item interactions must be modeled as a
complex network of multiple relations, not only a single relation. Recently
research on multi-relational factorization and hybrid recommender models has
shown that using extended meta-paths to capture additional information about
both users and items in the network can enhance the accuracy of recommendations
in such networks. Most of this work is focused on unweighted heterogeneous
networks, and to apply these techniques, weighted relations must be simplified
into binary ones. However, information associated with weighted edges, such as
user ratings, which may be crucial for recommendation, are lost in such
binarization. In this paper, we explore a random walk sampling method in which
the frequency of edge sampling is a function of edge weight, and apply this
generate extended meta-paths in weighted heterogeneous networks. With this
sampling technique, we demonstrate improved performance on multiple data sets
both in terms of recommendation accuracy and model generation efficiency
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