55 research outputs found
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems
The emergence of Pre-trained Language Models (PLMs) has achieved tremendous
success in the field of Natural Language Processing (NLP) by learning universal
representations on large corpora in a self-supervised manner. The pre-trained
models and the learned representations can be beneficial to a series of
downstream NLP tasks. This training paradigm has recently been adapted to the
recommendation domain and is considered a promising approach by both academia
and industry. In this paper, we systematically investigate how to extract and
transfer knowledge from pre-trained models learned by different PLM-related
training paradigms to improve recommendation performance from various
perspectives, such as generality, sparsity, efficiency and effectiveness.
Specifically, we propose a comprehensive taxonomy to divide existing PLM-based
recommender systems w.r.t. their training strategies and objectives. Then, we
analyze and summarize the connection between PLM-based training paradigms and
different input data types for recommender systems. Finally, we elaborate on
open issues and future research directions in this vibrant field.Comment: Accepted for publication at Transactions of the Association for
Computational Linguistics (TACL) in September 202
Evaluation of team dynamic in Norwegian projects for IT students
The need for teaching realistic software development in project courses has
increased in a global scale. It has always been challenges in cooperating
fast-changing software technologies, development methodologies and teamwork.
Moreover, such project courses need to be designed in the connection to
existing theoretical courses. We performed a large-scale research on student
performance in Software Engineering projects in Norwegian universities. This
paper investigates four aspects of team dynamics, which are team reflection,
leadership, decision making and task assignment in order to improve student
learning. Data was collected from student projects in 4 years at two
universities. We found that some leader's characteristics are perceived
differently for female and male leaders, including the perception of leaders as
skilful workers or visionaries. Leadership is still a challenging aspect to
teach, and assigned leadership is probably not the best way to learn. Students
is are performing well in task review, however, needs support while performing
task assignment. The result also suggests that task management to be done in
more fine-grained levels. It is also important to maintain an open and active
discussion to facilitate effective group decision makings
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
Neural Networks for Entity Matching: A Survey
Entity matching is the problem of identifying which records refer to the same real-world entity. It has been actively researched for decades, and a variety of different approaches have been developed. Even today, it remains a challenging problem, and there is still generous room for improvement. In recent years, we have seen new methods based upon deep learning techniques for natural language processing emerge.
In this survey, we present how neural networks have been used for entity matching. Specifically, we identify which steps of the entity matching process existing work have targeted using neural networks, and provide an overview of the different techniques used at each step. We also discuss contributions from deep learning in entity matching compared to traditional methods, and propose a taxonomy of deep neural networks for entity matching
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