1,373 research outputs found
Item Recommendation with Evolving User Preferences and Experience
Current recommender systems exploit user and item similarities by
collaborative filtering. Some advanced methods also consider the temporal
evolution of item ratings as a global background process. However, all prior
methods disregard the individual evolution of a user's experience level and how
this is expressed in the user's writing in a review community. In this paper,
we model the joint evolution of user experience, interest in specific item
facets, writing style, and rating behavior. This way we can generate individual
recommendations that take into account the user's maturity level (e.g.,
recommending art movies rather than blockbusters for a cinematography expert).
As only item ratings and review texts are observables, we capture the user's
experience and interests in a latent model learned from her reviews, vocabulary
and writing style. We develop a generative HMM-LDA model to trace user
evolution, where the Hidden Markov Model (HMM) traces her latent experience
progressing over time -- with solely user reviews and ratings as observables
over time. The facets of a user's interest are drawn from a Latent Dirichlet
Allocation (LDA) model derived from her reviews, as a function of her (again
latent) experience level. In experiments with five real-world datasets, we show
that our model improves the rating prediction over state-of-the-art baselines,
by a substantial margin. We also show, in a use-case study, that our model
performs well in the assessment of user experience levels
Interactive Reputation Systems - How to Cope with Malicious Behavior in Feedback Mechanisms
Early reputation systems use simple computation metrics that can easily be manipulated by malicious actors. Advanced computation models that mitigate their weaknesses, however, are non-transparent to the end-users thus lowering their understandability and the users’ trust towards the reputation system. The paper proposes the concept of interactive reputation systems that combine the cognitive capabilities of the user with the advantages of robust metrics while preserving the system’s transparency. Results of the evaluation show that interactive reputation systems increase both the users’ detection ability (robustness) and understanding of malicious behavior while avoiding trade-offs in usability
Sequential Recommendation with Diffusion Models
Generative models, such as Variational Auto-Encoder (VAE) and Generative
Adversarial Network (GAN), have been successfully applied in sequential
recommendation. These methods require sampling from probability distributions
and adopt auxiliary loss functions to optimize the model, which can capture the
uncertainty of user behaviors and alleviate exposure bias. However, existing
generative models still suffer from the posterior collapse problem or the model
collapse problem, thus limiting their applications in sequential
recommendation. To tackle the challenges mentioned above, we leverage a new
paradigm of the generative models, i.e., diffusion models, and present
sequential recommendation with diffusion models (DiffRec), which can avoid the
issues of VAE- and GAN-based models and show better performance. While
diffusion models are originally proposed to process continuous image data, we
design an additional transition in the forward process together with a
transition in the reverse process to enable the processing of the discrete
recommendation data. We also design a different noising strategy that only
noises the target item instead of the whole sequence, which is more suitable
for sequential recommendation. Based on the modified diffusion process, we
derive the objective function of our framework using a simplification technique
and design a denoise sequential recommender to fulfill the objective function.
As the lengthened diffusion steps substantially increase the time complexity,
we propose an efficient training strategy and an efficient inference strategy
to reduce training and inference cost and improve recommendation diversity.
Extensive experiment results on three public benchmark datasets verify the
effectiveness of our approach and show that DiffRec outperforms the
state-of-the-art sequential recommendation models
Smartphone picture organization: a hierarchical approach
We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin
Towards Graph-Aware Diffusion Modeling for Collaborative Filtering
Recovering masked feedback with neural models is a popular paradigm in
recommender systems. Seeing the success of diffusion models in solving
ill-posed inverse problems, we introduce a conditional diffusion framework for
collaborative filtering that iteratively reconstructs a user's hidden
preferences guided by its historical interactions. To better align with the
intrinsic characteristics of implicit feedback data, we implement forward
diffusion by applying synthetic smoothing filters to interaction signals on an
item-item graph. The resulting reverse diffusion can be interpreted as a
personalized process that gradually refines preference scores. Through graph
Fourier transform, we equivalently characterize this model as an anisotropic
Gaussian diffusion in the graph spectral domain, establishing both forward and
reverse formulations. Our model outperforms state-of-the-art methods by a large
margin on one dataset and yields competitive results on the others.Comment: 13 pages, 6 figure
A Survey on Fairness-aware Recommender Systems
As information filtering services, recommender systems have extremely
enriched our daily life by providing personalized suggestions and facilitating
people in decision-making, which makes them vital and indispensable to human
society in the information era. However, as people become more dependent on
them, recent studies show that recommender systems potentially own
unintentional impacts on society and individuals because of their unfairness
(e.g., gender discrimination in job recommendations). To develop trustworthy
services, it is crucial to devise fairness-aware recommender systems that can
mitigate these bias issues. In this survey, we summarise existing methodologies
and practices of fairness in recommender systems. Firstly, we present concepts
of fairness in different recommendation scenarios, comprehensively categorize
current advances, and introduce typical methods to promote fairness in
different stages of recommender systems. Next, after introducing datasets and
evaluation metrics applied to assess the fairness of recommender systems, we
will delve into the significant influence that fairness-aware recommender
systems exert on real-world industrial applications. Subsequently, we highlight
the connection between fairness and other principles of trustworthy recommender
systems, aiming to consider trustworthiness principles holistically while
advocating for fairness. Finally, we summarize this review, spotlighting
promising opportunities in comprehending concepts, frameworks, the balance
between accuracy and fairness, and the ties with trustworthiness, with the
ultimate goal of fostering the development of fairness-aware recommender
systems.Comment: 27 pages, 9 figure
Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
Recommender systems have been gaining increasing research attention over the
years. Most existing recommendation methods focus on capturing users'
personalized preferences through historical user-item interactions, which may
potentially violate user privacy. Additionally, these approaches often overlook
the significance of the temporal fluctuation in item popularity that can sway
users' decision-making. To bridge this gap, we propose Popularity-Aware
Recommender (PARE), which makes non-personalized recommendations by predicting
the items that will attain the highest popularity. PARE consists of four
modules, each focusing on a different aspect: popularity history, temporal
impact, periodic impact, and side information. Finally, an attention layer is
leveraged to fuse the outputs of four modules. To our knowledge, this is the
first work to explicitly model item popularity in recommendation systems.
Extensive experiments show that PARE performs on par or even better than
sophisticated state-of-the-art recommendation methods. Since PARE prioritizes
item popularity over personalized user preferences, it can enhance existing
recommendation methods as a complementary component. Our experiments
demonstrate that integrating PARE with existing recommendation methods
significantly surpasses the performance of standalone models, highlighting
PARE's potential as a complement to existing recommendation methods.
Furthermore, the simplicity of PARE makes it immensely practical for industrial
applications and a valuable baseline for future research.Comment: 9 pages, 5 figure
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