aggregate knowledge recommendations

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aggregate knowledge recommendations

Aggregating knowledge-aware graph neural network and adaptive

1 Introduction Inthe era of information overload, recommender systems play a pivotal role in various online services, which aim to match user interests with resource items [ 1 ]. One of the popular techniques used in recommender systems is Matrix Request PDF Simplifying Knowledge-Aware Aggregation for Knowledge Graph Collaborative Filtering Incorporating knowledge graph (KG) for Simplifying Knowledge-Aware Aggregation for Knowledge Graph

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A Multi-Granular Aggregation-Enhanced Knowledge

In this paper, we propose a new model, named A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation (MAKR), that relieves the sparsity of the network Abstract. Currently, recommender systems based on knowledge graph (KG) consider various aspects of the item to provide accurate recommendations. Many studies have Aggregating knowledge-aware graph neural network and adaptive

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Hierarchical Aggregation Based Knowledge Graph Embedding for

The existing recommendation models try many ways to improve the recommendation performance by combining knowledge graphs. Among these, the 2.1. Embedding-Based Recommendation Methods. The embedding-based recommendation methods use fruitful facts in the knowledge graph to enrich user representations and item Applied Sciences Free Full-Text CKGAT: Collaborative

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Neighbor-Augmented Knowledge Graph Attention Network for

This paper presents a new recommendation model: Neural Attention Network for Neighborhood Augmented Knowledge Graph (NKGAT). The model is Incorporating knowledge graphs into recommendation systems has attracted wide attention in various fields recently. A Knowledge graph contains abundant information with multi-type relations amongIterative heterogeneous graph learning for knowledge graph

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Aggregate Knowledge Recent News & Activity Crunchbase

Aggregate Knowledge is a media intelligence company that enables advertisers to reach the highest performing customers on a single platform.2015. TLDR. This work proposes SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most “useful” or “interesting”.Efficient Recommendation of Aggregate Data Visualizations

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Applied Sciences Free Full-Text A Knowledge Graph-Enhanced

In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KANR, a Recommendation systems have become based on graph neural networks (GNN) as many fields, and this is due to the advantages that represent this kind of neural networks compared to the classical ones; notably, the representation of concrete realities by taking the relationships between data into consideration and understanding them in a Deep GraphSAGE-based recommendation system: jumping knowledge

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Improving Aggregate Recommendation Diversity Using Ranking

Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize Knowledge graph (KG) has proven to improve recommendation performance. However, most efforts explore inter-entity relatedness by mining multi-hop relations on KG, thus failing to efficiently exploit these relations for enhanced user preference. To address this, we propose an end-to-end framework to improve the Improving recommender system via knowledge graph based

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GRE: A GAT-Based Relation Embedding Model of Knowledge

Besides, MKR is a more generalized framework than other knowledge graph-aware recommendation methods. RippleNet [ 24 ] draws on the strengths of embedding- and path-based recommendation methods. It progressively propagates users’ latent preferences over the set of randomly sampled KG entities and seeks their multi In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KANR, a Applied Sciences Free Full-Text A Knowledge Graph-Enhanced

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Improving aggregate recommendation diversity using ranking

Abstract. Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), otherIn order to make a recommendation, a recommender system typically first predicts a user's ratings for items and then recommends a list of items to the user which have high predicted ratings. Quality of predictions is measured by accuracy, that is, howEffective methods for increasing aggregate diversity in

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Deep Hybrid Knowledge Graph Embedding for Top-N Recommendation

Knowledge graphs (KGs) have proven to be effective in improving recommendation performance [7, 16].According to [], there are three categories of KG-based recommendation methods: path-based methods, embedding-based methods, and unified methods.Path-based methods make recommendations by building a KG which our knowledge our work introduces the first one-shot federated learning method for unsupervised recommendation. Work in federated recommendation systems, specifically matrix factorization [61, 62, 64] attempts to solve a problem that is similar to our work within collaborative filtering, with certain users being present in distributed matrices andFedSPLIT: One-Shot Federated Recommendation System Based

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(PDF) A Knowledge Graph-Enhanced Attention Aggregation Network

This is an end-to-end deep learning model using knowledge graph embedding to enhance the attention aggregation network for making recommendations. It consists of three main parts. The first is theRecommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize Improving Aggregate Recommendation Diversity Using Ranking-Based

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On improving aggregate recommendation diversity and novelty

Niemann K, Wolpers M (2013) A new collaborative filtering approach for increasing the aggregate diversity of recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 955---963 Google Scholar; Patil CB, Wagh RB (2013) Recommendation diversity for web In that way, we capture the real intent of query. We utilize a Compare-Aggregate model to implement the idea, and simulate the interactively attentive reading and thinking of human behavior. We also leverage external conceptual knowledge to enrich the model and fill the expression gap between query and document.A Compare-Aggregate Model with External Knowledge for Query

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Aggregately Diversified Bundle Recommendation via Popularity

Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, However, there have been no studies on aggregate diversity in bundle recommendation, while they have been intensively studied in item recommendation. Moreover,There are several factors which cause a low aggregate diversity score for a recommender system. One of these factors is popularity. As Fig. 1 shows recommender algorithms tend to recommend highly popular (i.e., highly rated) items which is one of the main sources of popularity bias in the recommendation lists. A successful algorithm Effective methods for increasing aggregate diversity in recommender

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Aggregate Knowledge Products, Competitors, Financials,

Aggregate Knowledge (AK) is a media intelligence company offering advertisers and agencies an exact science to pinpoint where to reach highest performing customers in a single platform. The company started as a recommendation engine in 2006, landed a few big contracts, then abruptly slowed down.

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