Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of th...Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.展开更多
Graphs are widely used for modeling complicated data such as social networks,chemical compounds,protein interactions and semantic web.To effiectively understand and utilize any collection of graphs,a graph database th...Graphs are widely used for modeling complicated data such as social networks,chemical compounds,protein interactions and semantic web.To effiectively understand and utilize any collection of graphs,a graph database that efficiently supports elementary querying mechanisms is crucially required.For example,Subgraph and Supergraph queries are important types of graph queries which have many applications in practice.A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems.Relational database management systems（RDBMSs） have repeatedly been shown to be able to efficiently host different types of data such as complex objects and XML data.RDBMSs derive much of their performance from sophisticated optimizer components which make use of physical properties that are specific to the relational model such as sortedness,proper join ordering and powerful indexing mechanisms.In this article,we study the problem of indexing and querying graph databases using the relational infrastructure.We present a purely relational framework for processing graph queries.This framework relies on building a layer of graph features knowledge which capture metadata and summary features of the underlying graph database.We describe different querying mechanisms which make use of the layer of graph features knowledge to achieve scalable performance for processing graph queries.Finally,we conduct an extensive set of experiments on real and synthetic datasets to demonstrate the efficiency and the scalability of our techniques.展开更多
文摘Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.
文摘Graphs are widely used for modeling complicated data such as social networks,chemical compounds,protein interactions and semantic web.To effiectively understand and utilize any collection of graphs,a graph database that efficiently supports elementary querying mechanisms is crucially required.For example,Subgraph and Supergraph queries are important types of graph queries which have many applications in practice.A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems.Relational database management systems（RDBMSs） have repeatedly been shown to be able to efficiently host different types of data such as complex objects and XML data.RDBMSs derive much of their performance from sophisticated optimizer components which make use of physical properties that are specific to the relational model such as sortedness,proper join ordering and powerful indexing mechanisms.In this article,we study the problem of indexing and querying graph databases using the relational infrastructure.We present a purely relational framework for processing graph queries.This framework relies on building a layer of graph features knowledge which capture metadata and summary features of the underlying graph database.We describe different querying mechanisms which make use of the layer of graph features knowledge to achieve scalable performance for processing graph queries.Finally,we conduct an extensive set of experiments on real and synthetic datasets to demonstrate the efficiency and the scalability of our techniques.
