SimRank

About: SimRank is a research topic. Over the lifetime, 250 publications have been published within this topic receiving 21163 citations.

Emotion Tag Based Music Retrieval Algorithm

Abstract: Music is to express emotions and interpreted by tags. Different emotion tags describe the same piece of music in different perspectives. This paper proposes a music retrieval algorithm which is based on the users’ emotion tags. First, we build a bi-partite graph, with tags on one side and music on the other, to compute the semantic similarity between the tags by T_SimRank. Second, we use the T_PageRank algorithm to get the music-popularity. Last, by taking the advantage of learning to rank, we combine many methods to get the final ranking results. Experimental results show that our method is better than the traditional cosine similarity and the Co_Tags similarity, and the fused method performs better than the single method.

Realtime Index-Free Single Source SimRank Processing on Web-Scale Graphs

Abstract: Given a graph G and a node u in G, a single source SimRank query evaluates the similarity between u and every node v in G. Existing approaches to single source SimRank computation incur either long query response time, or expensive pre-computation, which needs to be performed again whenever the graph G changes. Consequently, to our knowledge none of them is ideal for scenarios in which (i) query processing must be done in realtime, and (ii) the underlying graph G is massive, with frequent updates. Motivated by this, we propose SimPush, a novel algorithm that answers single source SimRank queries without any pre-computation, and at the same time achieves significantly higher query processing speed than even the fastest known index-based solutions. Further, SimPush provides rigorous result quality guarantees, and its high performance does not rely on any strong assumption of the underlying graph. Specifically, compared to existing methods, SimPush employs a radically different algorithmic design that focuses on (i) identifying a small number of nodes relevant to the query, and subsequently (ii) computing statistics and performing residue push from these nodes only. We prove the correctness of SimPush, analyze its time complexity, and compare its asymptotic performance with that of existing methods. Meanwhile, we evaluate the practical performance of SimPush through extensive experiments on 8 real datasets. The results demonstrate that SimPush consistently outperforms all existing solutions, often by over an order of magnitude. In particular, on a commodity machine, SimPush answers a single source SimRank query on a web graph containing over 133 million nodes and 5.4 billion edges in under 62 milliseconds, with 0.00035 empirical error, while the fastest index-based competitor needs 1.18 seconds.

SimRank and its variants in academic literature data: measures and evaluation

Abstract: SimRank is a well-known link-based similarity measure that can be applied on a citation graph to compute similarity of academic literature data. The intuition behind SimRank is that two objects are similar if they are referenced by similar objects. SimRank has attracted a growing interest in the areas of data mining and information retrieval recently. Despite of the current success of SimRank, it has some problems that negatively affect its effectiveness in similarity computation. In this paper, we discuss the three existing problems of SimRank, present SimRank variants that have been proposed to solve those problems, and evaluate the effectiveness of SimRank and its variants in similarity computation for academic literature data by conducting extensive experiments on a real-world dataset.

Collaborative filtering video recommendation method for considering user preference dynamic changes

Abstract: The invention discloses a collaborative filtering video recommendation method for considering user preference dynamic changes The method comprises the steps of data pre-processing, model training andsorting, wherein the data pre-processing is mainly that original data is processed to generate a formative leaning sample set required for model training; and a training model mainly learns user characteristics and video characteristics according to generated samples, and is mainly composed of a parameter matrix, a BPR model and a SimRank model When a system is ready to recommend videos to users, a recommendation engine firstly reads the users and videos recorded by a background and corresponding metadata into a pre-processing module; then a training module firstly initializes to-be-learnedcharacteristic parameters, BPR leaning and SimRank learning are carried out respectively on input corresponding leaning samples according to the data pre-processing module; and lastly, the videos aresorted and recommended according to the trained user characteristics and video characteristics The collaborative filtering video recommendation method for considering the user preference dynamic changes has the advantages that under the condition of not increasing the time complexity, the user preference is modeled dynamically, thereby improving the accuracy of recommendation

Composition Context-Based Web Services Similarity Measure

Abstract: Web services similarity measure is an important problem in service computing area, which is the technological foundation of service substitution, service discovery, service recommendation, and so on. Most of the existing works use a static description of services to measure the similarity between two services. However, the interaction information of Web services recorded in the historical compositions is totally neglected. In this paper, we propose a novel Web services similarity measure approach based on the notion of service composition context. Specifically, we first introduce three types of parameter correlations between service input and output parameters. These correlations can be obtained from existing services compositions. Based on parameter correlations, we propose the service composition context model. Through the composition context of a service, the composition context network is constructed using contexts of all services. Then, we propose to measure the similarity between any two services using the PersonalRank and SimRank++ algorithms by taking the obtained context network as input. By experiments, we analyze the characteristics of our proposed method and demonstrate that its accuracy is much better than the state-of-the-art approaches.