22 research outputs found
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation
Graph neural networks (GNNs) have gained prominence in recommendation systems
in recent years. By representing the user-item matrix as a bipartite and
undirected graph, GNNs have demonstrated their potential to capture short- and
long-distance user-item interactions, thereby learning more accurate preference
patterns than traditional recommendation approaches. In contrast to previous
tutorials on the same topic, this tutorial aims to present and examine three
key aspects that characterize GNNs for recommendation: (i) the reproducibility
of state-of-the-art approaches, (ii) the potential impact of graph topological
characteristics on the performance of these models, and (iii) strategies for
learning node representations when training features from scratch or utilizing
pre-trained embeddings as additional item information (e.g., multimodal
features). The goal is to provide three novel theoretical and practical
perspectives on the field, currently subject to debate in graph learning but
long been overlooked in the context of recommendation systems
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis
Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g.,
product images or descriptions) as items' side information to improve
recommendation accuracy. While most of such methods rely on factorization
models (e.g., MFBPR) as base architecture, it has been shown that MFBPR may be
affected by popularity bias, meaning that it inherently tends to boost the
recommendation of popular (i.e., short-head) items at the detriment of niche
(i.e., long-tail) items from the catalog. Motivated by this assumption, in this
work, we provide one of the first analyses on how multimodality in
recommendation could further amplify popularity bias. Concretely, we evaluate
the performance of four state-of-the-art MRSs algorithms (i.e., VBPR, MMGCN,
GRCN, LATTICE) on three datasets from Amazon by assessing, along with
recommendation accuracy metrics, performance measures accounting for the
diversity of recommended items and the portion of retrieved niche items. To
better investigate this aspect, we decide to study the separate influence of
each modality (i.e., visual and textual) on popularity bias in different
evaluation dimensions. Results, which demonstrate how the single modality may
augment the negative effect of popularity bias, shed light on the importance to
provide a more rigorous analysis of the performance of such models
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation
In multimodal-aware recommendation, the extraction of meaningful multimodal
features is at the basis of high-quality recommendations. Generally, each
recommendation framework implements its multimodal extraction procedures with
specific strategies and tools. This is limiting for two reasons: (i) different
extraction strategies do not ease the interdependence among multimodal
recommendation frameworks; thus, they cannot be efficiently and fairly
compared; (ii) given the large plethora of pre-trained deep learning models
made available by different open source tools, model designers do not have
access to shared interfaces to extract features. Motivated by the outlined
aspects, we propose Ducho, a unified framework for the extraction of multimodal
features in recommendation. By integrating three widely-adopted deep learning
libraries as backends, namely, TensorFlow, PyTorch, and Transformers, we
provide a shared interface to extract and process features where each backend's
specific methods are abstracted to the end user. Noteworthy, the extraction
pipeline is easily configurable with a YAML-based file where the user can
specify, for each modality, the list of models (and their specific
backends/parameters) to perform the extraction. Finally, to make Ducho
accessible to the community, we build a public Docker image equipped with a
ready-to-use CUDA environment and propose three demos to test its
functionalities for different scenarios and tasks. The GitHub repository and
the documentation is accessible at this link:
https://github.com/sisinflab/Ducho
Formalizing Multimedia Recommendation through Multimodal Deep Learning
Recommender systems (RSs) offer personalized navigation experiences on online
platforms, but recommendation remains a challenging task, particularly in
specific scenarios and domains. Multimodality can help tap into richer
information sources and construct more refined user/item profiles for
recommendations. However, existing literature lacks a shared and universal
schema for modeling and solving the recommendation problem through the lens of
multimodality. This work aims to formalize a general multimodal schema for
multimedia recommendation. It provides a comprehensive literature review of
multimodal approaches for multimedia recommendation from the last eight years,
outlines the theoretical foundations of a multimodal pipeline, and demonstrates
its rationale by applying it to selected state-of-the-art approaches. The work
also conducts a benchmarking analysis of recent algorithms for multimedia
recommendation within Elliot, a rigorous framework for evaluating recommender
systems. The main aim is to provide guidelines for designing and implementing
the next generation of multimodal approaches in multimedia recommendation
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
EvalRS aims to bring together practitioners from industry and academia to
foster a debate on rounded evaluation of recommender systems, with a focus on
real-world impact across a multitude of deployment scenarios. Recommender
systems are often evaluated only through accuracy metrics, which fall short of
fully characterizing their generalization capabilities and miss important
aspects, such as fairness, bias, usefulness, informativeness. This workshop
builds on the success of last year's workshop at CIKM, but with a broader scope
and an interactive format.Comment: EvalRS 2023 will be a workshop hosted at KDD2
A Topology-aware Analysis of Graph Collaborative Filtering
The successful integration of graph neural networks into recommender systems
(RSs) has led to a novel paradigm in collaborative filtering (CF), graph
collaborative filtering (graph CF). By representing user-item data as an
undirected, bipartite graph, graph CF utilizes short- and long-range
connections to extract collaborative signals that yield more accurate user
preferences than traditional CF methods. Although the recent literature
highlights the efficacy of various algorithmic strategies in graph CF, the
impact of datasets and their topological features on recommendation performance
is yet to be studied. To fill this gap, we propose a topology-aware analysis of
graph CF. In this study, we (i) take some widely-adopted recommendation
datasets and use them to generate a large set of synthetic sub-datasets through
two state-of-the-art graph sampling methods, (ii) measure eleven of their
classical and topological characteristics, and (iii) estimate the accuracy
calculated on the generated sub-datasets considering four popular and recent
graph-based RSs (i.e., LightGCN, DGCF, UltraGCN, and SVD-GCN). Finally, the
investigation presents an explanatory framework that reveals the linear
relationships between characteristics and accuracy measures. The results,
statistically validated under different graph sampling settings, confirm the
existence of solid dependencies between topological characteristics and
accuracy in the graph-based recommendation, offering a new perspective on how
to interpret graph CF
How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation
Recommender Systems have shown to be an efective way to alleviate the over-choice problem and provide
accurate and tailored recommendations. However, the impressive number of proposed recommendation
algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental
evaluation particularly challenging. ELLIOT is a comprehensive recommendation framework that aims
to run and reproduce an entire experimental pipeline by processing a simple confguration fle. The
framework loads, flters, and splits the data considering a vast set of strategies. Then, it optimizes
hyperparameters for several recommendation algorithms, selects the best models, compares them with
the baselines, computes metrics spanning from accuracy to beyond-accuracy, bias, and fairness, and
conducts statistical analysis. The aim is to provide researchers a tool to ease all the experimental
evaluation phases (and make them reproducible), from data reading to results collection. ELLIOT is
freely available on GitHub at https://github.com/sisinflab/ellio