GInRec: A Gated Architecture for Inductive Recommendation using Knowledge Graphs

We have witnessed increasing interest in exploiting KGs to integrate contextual knowledge in recommender systems in addition to user-item interactions, e.g., ratings. Yet, most methods are transductive, i.e., they represent instances seen during training as low-dimensionality vectors but cannot do so for unseen instances. Hence, they require heavy retraining every time new items or users are added. Conversely, inductive methods promise to solve these issues. KGs enhance inductive recommendation by offering information on item-entity relationships, whereas existing inductive methods rely purely on interactions, which makes recommendations for users with few interactions sub-optimal and even impossible for new items. In this work, we investigate the actual ability of inductive methods exploiting both the structure and the data represented by KGs. Hence, we propose GInRec, a state-of-the-art method that uses a graph neural network with relation-specific gates and a KG to provide better recommendations for new users and items than related inductive methods. As a result, we re-evaluate state-of-the-art methods, identify better evaluation protocols, highlight unwarranted conclusions from previous proposals, and showcase a novel, stronger architecture for this task. The source code is available at: https://github.com/theisjendal/kars2023-recommendation-framework

An Information Theoretic Analysis of Rooted-Tree Based Secure Multicast Key Distribution Schemes

Several variations of rooted tree based solutions have been recently proposed for member revocation in multicast communications. In this paper, we show that by assigning probabilities for member revocations, the optimality, correctness, and the system requirements of some of these schemes can be systematically studied using information theoretic concepts. Specifically, we show that the optimal average number of keys per member in a rooted tree is related to the entropy of the member revocation event.Using our derivations, we show that (a) the key assignments in correspond to the maximum entropy solution, (b) and direct application of source coding will lead to member collusion (we present recently proposed solutions as examples of this) and a general criteria that admits member collusion. We also show the relationship between entropy of member revocation event and key length.Crypto '99 :: 19th Annual IACR Crypto Conference</i

The Yelp Collaborative Knowledge Graph

This is the The Yelp Collaborative Knowledge Graph (YCKG) - a transformation of the Yelp Open Dataset into RDF format using Y2KG. Paper Abstract The Yelp Open Dataset (YOD) contains data about businesses, reviews, and users from the Yelp website and is available for research purposes. This dataset has been widely used to develop and test Recommender Systems (RS), especially those using Knowledge Graphs (KGs), e.g., integrating taxonomies, product categories, business locations, and social network information. Unfortunately, researchers applied naive or wrong mappings while converting YOD in KGs, consequently obtaining unrealistic results. Among the various issues, the conversion processes usually do not follow state-of-the-art methodologies, fail to properly link to other KGs and reuse existing vocabularies. In this work, we overcome these issues by introducing Y2KG, a utility to convert the Yelp dataset into a KG. Y2KG consists of two components. The first is a dataset including (1) a vocabulary that extends Schema.org with properties to describe the concepts in YOD and (2) mappings between the Yelp entities and Wikidata. The second component is a set of scripts to transform YOD in RDF and obtain the Yelp Collaborative Knowledge Graph (YCKG). The design of Y2KG was driven by 16 core competency questions. YCKG includes 150k businesses and 16.9M reviews from 1.9M distinct real users, resulting in over 244 million triples (with 144 distinct predicates) for about 72 million resources, with an average in-degree and out-degree of 3.3 and 12.2, respectively. Links Latest GitHub release: https://github.com/MadsCorfixen/The-Yelp-Collaborative-Knowledge-Graph/releases/latest PURL domain: https://purl.archive.org/domain/yckg Files Graph Data Triple Files One sample file for each of the Yelp domains (Businesses, Users, Reviews, Tips and Checkins), each containing 20 entities. yelp_schema_mappings.nt.gz containing the mappings from Yelp categories to Schema things. schema_hierarchy.nt.gz containing the full hierarchy of the mapped Schema things. yelp_wiki_mappings.nt.gz containing the mappings from Yelp categories to Wikidata entities. wikidata_location_mappings.nt.gz containing the mappings from Yelp locations to Wikidata entities. Graph Metadata Triple Files yelp_categories.ttl contains metadata for all Yelp categories. yelp_entities.ttl contains metadata regarding the dataset yelp_vocabulary.ttl contains metadata on the created Yelp vocabulary and properties. Utility Files yelp_category_schema_mappings.csv. This file contains the 310 mappings from Yelp categories to Schema types. These mappings have been manually verified to be correct. yelp_predicate_schema_mappings.csv. This file contains the 14 mappings from Yelp attributes to Schema properties. These mappings are manually found. ground_truth_yelp_category_schema_mappings.csv. This file contains the ground truth, based on 200 manually verified mappings from Yelp categories to Schema things. The ground truth mappings were used to calculate precision and recall for the semantic mappings. manually_split_categories.csv. This file contains all Yelp categories containing either a &amp; or /, and their manually split versions. The split versions have been used in the semantic mappings to Schema things