1,084 research outputs found
Three Essays on Big Data Consumer Analytics in E-Commerce
Consumers are increasingly spending more time and money online. Business
to consumer e-commerce is growing on average of 20 percent each year and
has reached 1.5 trillion dollars globally in 2014. Given the scale and growth
of consumer online purchase and usage data, firms\u27 ability to understand
and utilize this data is becoming an essential competitive strategy.
But, large-scale data analytics in e-commerce is still at its nascent stage and there
is much to be learned in all aspects of e-commerce. Successful analytics on big data often require a combination of both data mining and econometrics: data mining to reduce or structure
(from unstructured data such as text, photo, and video) large-scale data
and econometric analyses to truly understand and assign causality to interesting
patterns. In my dissertation, I study how firms can better utilize big data
analytics and specific applications of machine learning techniques for improved
e-commerce using theory-driven econometrical and experimental studies. I
show that e-commerce managers can now formulate data-driven strategies for
many aspect of business including cross-selling via recommenders on sales
sites to increasing brand awareness and leads via social media content-engineered-marketing.
These results are readily actionable with far-reaching economical consequences
Hybrid Recommender Systems: A Systematic Literature Review
Recommender systems are software tools used to generate and provide suggestions for items
and other entities to the users by exploiting various strategies. Hybrid recommender systems
combine two or more recommendation strategies in different ways to benefit from their complementary
advantages. This systematic literature review presents the state of the art in hybrid
recommender systems of the last decade. It is the first quantitative review work completely focused
in hybrid recommenders. We address the most relevant problems considered and present
the associated data mining and recommendation techniques used to overcome them. We also
explore the hybridization classes each hybrid recommender belongs to, the application domains,
the evaluation process and proposed future research directions. Based on our findings, most of
the studies combine collaborative filtering with another technique often in a weighted way. Also
cold-start and data sparsity are the two traditional and top problems being addressed in 23 and
22 studies each, while movies and movie datasets are still widely used by most of the authors.
As most of the studies are evaluated by comparisons with similar methods using accuracy metrics,
providing more credible and user oriented evaluations remains a typical challenge. Besides
this, newer challenges were also identified such as responding to the variation of user context,
evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid
recommenders represent a good basis with which to respond accordingly by exploring newer
opportunities such as contextualizing recommendations, involving parallel hybrid algorithms,
processing larger datasets, etc
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Understanding and Mitigating Multi-sided Exposure Bias in Recommender Systems
Fairness is a critical system-level objective in recommender systems that has
been the subject of extensive recent research. It is especially important in
multi-sided recommendation platforms where it may be crucial to optimize
utilities not just for the end user, but also for other actors such as item
sellers or producers who desire a fair representation of their items. Existing
solutions do not properly address various aspects of multi-sided fairness in
recommendations as they may either solely have one-sided view (i.e. improving
the fairness only for one side), or do not appropriately measure the fairness
for each actor involved in the system. In this thesis, I aim at first
investigating the impact of unfair recommendations on the system and how these
unfair recommendations can negatively affect major actors in the system. Then,
I seek to propose solutions to tackle the unfairness of recommendations. I
propose a rating transformation technique that works as a pre-processing step
before building the recommendation model to alleviate the inherent popularity
bias in the input data and consequently to mitigate the exposure unfairness for
items and suppliers in the recommendation lists. Also, as another solution, I
propose a general graph-based solution that works as a post-processing approach
after recommendation generation for mitigating the multi-sided exposure bias in
the recommendation results. For evaluation, I introduce several metrics for
measuring the exposure fairness for items and suppliers, and show that these
metrics better capture the fairness properties in the recommendation results. I
perform extensive experiments to evaluate the effectiveness of the proposed
solutions. The experiments on different publicly-available datasets and
comparison with various baselines confirm the superiority of the proposed
solutions in improving the exposure fairness for items and suppliers.Comment: Doctoral thesi
Popularity Bias as Ethical and Technical Issue in Recommendation: A Survey
Recommender Systems have become omnipresent in our ev- eryday life, helping us making decisions and navigating in the digital world full of information. However, only recently researchers have started discovering undesired and harmful effects of automated recommendation and began questioning how fair and ethical these systems are, while in- fluencing our day-to-day decision making, shaping our online behaviour and tastes. In the latest research works, various biases and phenomena like filter bubbles and echo chambers have been uncovered among the resulting effects of recommender systems and rigorous work has started on solving these issues. In this narrative survey, we investigate the emer- gence and progression of research on one of the potential types of biases in recommender systems, i.e. Popularity Bias. Many recommender al- gorithms have been shown to favor already popular items, hence giving them even more exposure, which can harm fairness and diversity on the platforms using such systems. Such a problem becomes even more com- plicated if the object of recommendation is not just products and content, but people, their work and services. This survey describes the progress in this field of study, highlighting the advancements and identifying the gaps in the research, where additional effort and attention is necessary to minimize the harmful effect and make sure that such systems are build in a fair and ethical way
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