Titlebook: Advances on Graph-Based Approaches in Information Retrieval; First International Ludovico Boratto,Daniele Malitesta,Erasmo Purifica Confer

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,Identifying Shopping Intent in?Product QA for?Proactive Recommendations,has been conducted in question answering for voice search, little attention has been paid on how to enable proactive recommendations from a voice assistant to its users. This is a highly challenging problem that often leads to user friction, mainly due to recommendations provided to the users at the

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,KGUF: Simple Knowledge-Aware Graph-Based Recommender with?User-Based Semantic Features Filtering,y Graph Collaborative Filtering (GCF). Following the same GNNs wave, recommender systems exploiting Knowledge Graphs (KGs) have also been successfully empowered by the GCF rationale to combine the representational power of GNNs with the semantics conveyed by KGs, giving rise to Knowledge-aware Graph

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,The Effectiveness of?Graph Contrastive Learning on?Mathematical Information Retrieval,l aspect of Mathematical Information Retrieval (MIR). Our findings reveal that this simple approach consistently exceeds the performance of the current leading formula retrieval model, TangentCFT. To support ongoing research and development in this field, we have made our source code accessible to t

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,The Impact of?Source-Target Node Distance on?Vicious Adversarial Attacks in?Social Network Recommenilor suggestions for new contacts. However, these systems are vulnerable to adversarial attacks orchestrated by malicious users using frameworks to manipulate recommendations artificially. In particular, frameworks such as SAVAGE have proven that conducting efficient and effective . vicious attacks

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https://doi.org/10.1007/978-3-031-31663-0 infer latent user behavior patterns inferred from user’s past shopping history. We propose features that capture the user’s latent shopping behavior from their purchase history, and combine them using a novel Mixture-of-Experts (MoE) model. Our evaluation shows that the proposed approach is able to

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A Survey of Recent GRAND Variantsncise item representation. Driven by the assumptions above, we propose KGUF, a KGCF model that learns latent representations of semantic features in the KG to better define the item profile. By leveraging user profiles through decision trees, KGUF effectively retains only those features relevant to

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