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

Bifidobacterial transfer from mother to child as examined by an animal model

Bifidobacteria commonly constitute the most abundant group of microorganisms in the healthy infant gut. Their intestinal establishment is believed to be maternally driven, and their acquisition has even been postulated to occur during pregnancy. In the current study, we evaluated bifidobacterial mother-to infant transmission events in a rat model by means of quantitative PCR (qPCR), as well as by Internally Transcribed Spacer (ITS) bifidobacterial profiling. The occurrence of strains supplied by mothers during pregnancy to their corresponding newborns was observed and identified by analysis immediately following C-section delivery. These findings provide intriguing support for the existence of an unknown route to facilitate bifidobacterial transfer during the very early stages of life

Angiostatin anti-angiogenesis requires IL-12: The innate immune system as a key target

© 2009 Albini et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Bifidobacterial Transfer from Mother to Child as Examined by an Animal Model

Bifidobacteria commonly constitute the most abundant group of microorganisms in the healthy infant gut. Their intestinal establishment is believed to be maternally driven, and their acquisition has even been postulated to occur during pregnancy. In the current study, we evaluated bifidobacterial mother-to infant transmission events in a rat model by means of quantitative PCR (qPCR), as well as by Internally Transcribed Spacer (ITS) bifidobacterial profiling. The occurrence of strains supplied by mothers during pregnancy to their corresponding newborns was observed and identified by analysis immediately following C-section delivery. These findings provide intriguing support for the existence of an unknown route to facilitate bifidobacterial transfer during the very early stages of life

Hierarchical Effects of Lactic Fermentation and Grain Germination on the Microbial and Metabolomic Profile of Rye Doughs

A multi-omics approach was adopted to investigate the impact of lactic acid fermentation and seed germination on the composition and physicochemical properties of rye doughs. Doughs were prepared with either native or germinated rye flour and fermented with Saccharomyces cerevisiae, combined or not with a sourdough starter including Limosilactobacillus fermentum, Weissella confusa and Weissella cibaria. LAB fermentation significantly increased total titrable acidity and dough rise regardless of the flour used. Targeted metagenomics revealed a strong impact of germination on the bacterial community profile of sprouted rye flour. Doughs made with germinated rye displayed higher levels of Latilactobacillus curvatus, while native rye doughs were associated with higher proportions of Lactoplantibacillus plantarum. The oligosaccharide profile of rye doughs indicated a lower carbohydrate content in native doughs as compared to the sprouted counterparts. Mixed fermentation promoted a consistent decrease in both monosaccharides and low-polymerization degree (PD)-oligosaccharides, but not in high-PD carbohydrates. Untargeted metabolomic analysis showed that native and germinated rye doughs differed in the relative abundance of phenolic compounds, terpenoids, and phospholipids. Sourdough fermentation promoted the accumulation of terpenoids, phenolic compounds and proteinogenic and non-proteinogenic amino acids. Present findings offer an integrated perspective on rye dough as a multi-constituent system and on cereal-sourced bioactive compounds potentially affecting the functional properties of derived food products

Mobilome and Resistome Reconstruction from Genomes Belonging to Members of the Bifidobacterium Genus

Specific members of the genus Bifidobacterium are among the first colonizers of the human/animal gut, where they act as important intestinal commensals associated with host health. As part of the gut microbiota, bifidobacteria may be exposed to antibiotics, used in particular for intrapartum prophylaxis, especially to prevent Streptococcus infections, or in the very early stages of life after the birth. In the current study, we reconstructed the in silico resistome of the Bifidobacterium genus, analyzing a database composed of 625 bifidobacterial genomes, including partial assembled strains with less than 100 genomic sequences. Furthermore, we screened bifidobacterial genomes for mobile genetic elements, such as transposases and prophage-like elements, in order to investigate the correlation between the bifido-mobilome and the bifido-resistome, also identifying genetic insertion hotspots that appear to be prone to horizontal gene transfer (HGT) events. These insertion hotspots were shown to be widely distributed among analyzed bifidobacterial genomes, and suggest the acquisition of antibiotic resistance genes through HGT events. These data were further corroborated by growth experiments directed to evaluate bacitracin A resistance in Bifidobacterium spp., a property that was predicted by in silico analyses to be part of the HGT-acquired resistome

Sparse Embeddings for Recommender Systems with Knowledge Graphs

Collaborative filtering models have undoubtedly dominated the scene of recommender systems. However, these methods do not take into account valuable item characteristics. On the other side, content-based algorithms only use this kind of information and may fail to generalize. Some collaborative filtering techniques have recently used side information about items, but they end up being huge models using thousands of features for modeling a single user-item interaction. In this paper, we present KGFlex, a sparse and expressive model based on feature embeddings. KGFlex studies which features are considered by each user when consuming an item. Then, it models each user-item interaction as a factorized entropy-driven combination of the only item features relevant to the user. An extensive experimental evaluation shows the approach's effectiveness, considering the recommendation results' accuracy, diversity, and induced bias

Addressing Privacy in Recommender Systems with Federated Learning

In recent years, recommender systems have successfully assisted user decision-making in various user-centered applications. In such scenarios, the modern approaches are based on collecting user-sensitive preferences. However, data collection is crucial since users now worry about the related privacy risks when sharing their data. This work presents a recommendation approach based on the Federated Learning paradigm, a distributed privacy-preserving approach to the recommendation. Here, users collaborate on the training while still controlling the amount of the shared sensitive data. This paper presents FPL: a pair-wise learning-to-rank approach based on Federated Learning. We show that it puts users in control of their data and reveals recommendation performance competing with centralized state-of-the-art approaches. The public implementation is available at https://split.to/sisinflab-fpl

Sparse Feature Factorization for Recommender Systems with Knowledge Graphs

Deep Learning and factorization-based collaborative filtering recommendation models have undoubtedly dominated the scene of recommender systems in recent years. However, despite their outstanding performance, these methods require a training time proportional to the size of the embeddings and it further increases when also side information is considered for the computation of the recommendation list. In fact, in these cases we have that with a large number of high-quality features, the resulting models are more complex and difficult to train. This paper addresses this problem by presenting KGFlex: a sparse factorization approach that grants an even greater degree of expressiveness. To achieve this result, KGFlex analyzes the historical data to understand the dimensions the user decisions depend on (e.g., movie direction, musical genre, nationality of book writer). KGFlex represents each item feature as an embedding and it models user-item interactions as a factorized entropy-driven combination of the item attributes relevant to the user. KGFlex facilitates the training process by letting users update only those relevant features on which they base their decisions. In other words, the user-item prediction is mediated by the user's personal view that considers only relevant features. An extensive experimental evaluation shows the approach's effectiveness, considering the recommendation results' accuracy, diversity, and induced bias. The public implementation of KGFlex is available at https://split.to/kgflex

Inferring User Decision-Making Processes in Recommender Systems with Knowledge Graphs

This paper proposes a sparse factorization approach, KGFlex, that represents each item feature as an embedding. With KGFlex, the user-item interactions are a factorized combination of the item features relevant to the user. An entropy-driven module drives the training considering only the feature involved in the user's decision-making process. Extensive experiments confirm the approach's effectiveness, considering the ranking accuracy, diversity, and induced bias. The public implementation of KGFlex is available at https://split.to/kgflex. © 2022 CEUR-WS. All rights reserved