788 research outputs found
Toward a Robust Diversity-Based Model to Detect Changes of Context
Being able to automatically and quickly understand the user context during a
session is a main issue for recommender systems. As a first step toward
achieving that goal, we propose a model that observes in real time the
diversity brought by each item relatively to a short sequence of consultations,
corresponding to the recent user history. Our model has a complexity in
constant time, and is generic since it can apply to any type of items within an
online service (e.g. profiles, products, music tracks) and any application
domain (e-commerce, social network, music streaming), as long as we have
partial item descriptions. The observation of the diversity level over time
allows us to detect implicit changes. In the long term, we plan to characterize
the context, i.e. to find common features among a contiguous sub-sequence of
items between two changes of context determined by our model. This will allow
us to make context-aware and privacy-preserving recommendations, to explain
them to users. As this is an ongoing research, the first step consists here in
studying the robustness of our model while detecting changes of context. In
order to do so, we use a music corpus of 100 users and more than 210,000
consultations (number of songs played in the global history). We validate the
relevancy of our detections by finding connections between changes of context
and events, such as ends of session. Of course, these events are a subset of
the possible changes of context, since there might be several contexts within a
session. We altered the quality of our corpus in several manners, so as to test
the performances of our model when confronted with sparsity and different types
of items. The results show that our model is robust and constitutes a promising
approach.Comment: 27th IEEE International Conference on Tools with Artificial
Intelligence (ICTAI 2015), Nov 2015, Vietri sul Mare, Ital
Intent-Aware Contextual Recommendation System
Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding the thought process of the user. An intelligent recommender
system is not only useful for the user but also for businesses which want to
learn the tendencies of their users. Finding out tendencies or intents of a
user is a difficult problem to solve.
Keeping this in mind, we sought out to create an intelligent system which
will keep track of the user's activity on a web-application as well as
determine the intent of the user in each session. We devised a way to encode
the user's activity through the sessions. Then, we have represented the
information seen by the user in a high dimensional format which is reduced to
lower dimensions using tensor factorization techniques. The aspect of intent
awareness (or scoring) is dealt with at this stage. Finally, combining the user
activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative
recommendation techniques to show the top-k recommendations to the user. A
provision for feedback is also envisioned in the current system which informs
the model to update the various weights in the recommender system. Our overall
model aims to combine both frequency-based and context-based recommendation
systems and quantify the intent of a user to provide better recommendations.
We ran experiments on real-world timestamped user activity data, in the
setting of recommending reports to the users of a business analytics tool and
the results are better than the baselines. We also tuned certain aspects of our
model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big
Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining
(ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field
cannot be longer than 1,920 characters," the abstract appearing here is
slightly shorter than the one in the PDF fil
Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review
A filter bubble refers to the phenomenon where Internet customization
effectively isolates individuals from diverse opinions or materials, resulting
in their exposure to only a select set of content. This can lead to the
reinforcement of existing attitudes, beliefs, or conditions. In this study, our
primary focus is to investigate the impact of filter bubbles in recommender
systems. This pioneering research aims to uncover the reasons behind this
problem, explore potential solutions, and propose an integrated tool to help
users avoid filter bubbles in recommender systems. To achieve this objective,
we conduct a systematic literature review on the topic of filter bubbles in
recommender systems. The reviewed articles are carefully analyzed and
classified, providing valuable insights that inform the development of an
integrated approach. Notably, our review reveals evidence of filter bubbles in
recommendation systems, highlighting several biases that contribute to their
existence. Moreover, we propose mechanisms to mitigate the impact of filter
bubbles and demonstrate that incorporating diversity into recommendations can
potentially help alleviate this issue. The findings of this timely review will
serve as a benchmark for researchers working in interdisciplinary fields such
as privacy, artificial intelligence ethics, and recommendation systems.
Furthermore, it will open new avenues for future research in related domains,
prompting further exploration and advancement in this critical area.Comment: 21 pages, 10 figures and 5 table
Beyond the ranked list: User-driven exploration and diversification of social recommendation
The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this paper, we present two attempts at creating a visual diversity-enhanced interface that presents recommendations beyond a simple ranked list. Our goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. We show that the users examined a diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users' subjective evaluations show significant improvement in many user-centric metrics. Experiences are discussed that shed light on avenues for future interface designs
Digital user's decision journey
The landscape of the Internet is continually evolving. This creates huge opportunities for different industries to optimize vital channels online, resulting in various-forms of new Internet services. As a result, digital users are interacting with many digital systems and they are exhibiting dynamic behaviors. Their shopping behaviors are drastically different today than it used to be, with offline and online shopping interacting with each other. They have many channels to access online media but their consumption patterns on different channels are quite different. They do philanthropy online to help others but their heterogeneous motivations and different fundraising campaigns leads to distinct path-to-contribution. Understanding the digital user’s decision making process behind their dynamic behaviors is critical as they interact with various digital systems for the firms to improve user experience and improve their bottom line. In this thesis, I study digital users’ decision journeys and the corresponding digital technology firms’ strategies using inter-disciplinary approaches that combine econometrics, economic structural modeling and machine learning. The uncovered decision journey not only offer empirical managerial insights but also provide guideline for introducing intervention to better serve digital users
Report on the Information Retrieval Festival (IRFest2017)
The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017
Improving End-to-End Sequential Recommendations with Intent-aware Diversification
Sequential Recommendation (SRs) that capture users' dynamic intents by
modeling user sequential behaviors can recommend closely accurate products to
users. Previous work on SRs is mostly focused on optimizing the recommendation
accuracy, often ignoring the recommendation diversity, even though it is an
important criterion for evaluating the recommendation performance. Most
existing methods for improving the diversity of recommendations are not ideally
applicable for SRs because they assume that user intents are static and rely on
post-processing the list of recommendations to promote diversity. We consider
both recommendation accuracy and diversity for SRs by proposing an end-to-end
neural model, called Intent-aware Diversified Sequential Recommendation (IDSR).
Specifically, we introduce an Implicit Intent Mining module (IIM) into SRs to
capture different user intents reflected in user behavior sequences. Then, we
design an Intent-aware Diversity Promoting (IDP) loss to supervise the learning
of the IIM module and force the model to take recommendation diversity into
consideration during training. Extensive experiments on two benchmark datasets
show that IDSR significantly outperforms state-of-the-art methods in terms of
recommendation diversity while yielding comparable or superior recommendation
accuracy
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