15 research outputs found
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
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
Bio-propylene glycol as value-added product from Epicerol® process
The production of chemicals from bio-based feedstocks is an emerging field of research in both industrial and academic communities. Here we present the synthesis of propylene glycol through catalytic hydrogenolysis of glycidol, obtained using a side-stream from the bio-based epichlorohydrin production plant, over Pd/C catalyst. In particular, we show the prominent effect of the acidic resin Amberlyst-15 in the selective and quantitative conversion of glycidol that permits to reach a TOF value of 162 h−1. Propylene glycol is obtained with high yields and selectivity (> 99%) in only 1 h under mild reaction conditions. The effect of solvent is also investigated giving interesting results on the reaction selectivity. The catalytic system (Pd/C + Amberlyst-15) shows a good recyclability also after seven reaction cycles reaching high performances in term of conversion and selectivity. This allowed minimizing the amount of waste and enhancing the efficiency of the whole system
Particle uptake by filter-feeding macrofoulers from the Mar Grande of Taranto (Mediterranean Sea, Italy): potential as microplastic pollution bioremediators
Microplastics (MPs) are a serious threat to the marine environment affecting ecosystem functioning and biodiversity. There is a vast literature about the uptake of MPs at different trophic levels, mainly focused on ecotoxicological effects in commercially relevant species. Little is still known about possible strategies to face MP pollution. Bioremediation is recently gaining attention in this framework. The clearance rate and particle
retention of Sabella spallanzanii, Mytilus galloprovincialis, Phallusia mammillata, Paraleucilla magna at three MP
concentrations (C1: 1.4 â‹… 101 p/L; C2: 1.4 â‹… 102 p/L; C3: 1.4 â‹… 103 p/L) were investigated to test their potential as
MP remover. Digestion protocol removed 98 % of tissues simplifying the MP quantification. P. magna clearance
rate decreased with increasing concentration while P. mammillata showed no significant variations. S. spallanzanii and M. galloprovincialis instead exhibited the highest values of clearance rate. Yet, unlike mussels, S. spallanzanii can inhibit particle return to the surrounding water storing them in the tube, resulting to be the best candidate for bioremediation purposes