Rank Learning Methods Research Articles

VAE*: A Novel Variational Autoencoder via Revisiting Positive and Negative Samples for Top- N Recommendation

Due to the easy access, implicit feedback is often used for recommender systems. Compared with point-wise learning and pair-wise learning methods, list-wise rank learning methods have superior performance for top- \(N\) recommendation. Recent solutions, especially the list-wise methods, simply treat all interacted items of a user as equally important positives and annotate all no-interaction items of a user as negatives. For the list-wise approaches, we argue that this annotation scheme of implicit feedback is over-simplified due to the sparsity and missing fine-grained labels of the feedback data. To overcome this issue, we revisit the so-called positive and negative samples. First, considering the loss function of list-wise ranking, we analyze the impact of false positives and negatives theoretically. Second, based on the observation, we propose a self-adjusting credibility weight mechanism to re-weigh the positive samples and exploit the higher-order relation based on item–item matrix to sample the critical negative samples. In order to prevent the introduction of noise, we design a pruning strategy for critical negatives. Besides, to combine the reconstruction loss function for the positive samples and critical negative samples, we develop a simple yet effective VAEs framework with linear structure, which abandons the complex non-linear structure. Extensive experiments are conducted on six public real-world datasets. The results demonstrate that, our VAE* outperforms other VAE-based models by a large margin. Besides, we also verify the effect of denoising positives and exploring critical negatives by ablation study.

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

Classification and saliency detection by semi-supervised low-rank representation

In the area of pattern recognition, Low Rank Representation (LRR) is an efficient method in recovering the subspace structure of the dataset. However, LRR is unsupervised. Without any label information, LRR constructs an informative graph which is then combined with the mature graph-based semi-supervised learning (GSSL) framework to complete the classification task. In this paper, we propose a new low rank learning method which constructs the low rank representation matrix utilizing label information to obtain a more informative graph. This method integrates the low rank graph construction and the label information propagation processes together. Thus the optimization of the low rank representation and the soft label prediction function are calculated iteratively at the same time. We name this method as Semi-Supervised Low Rank Learning (SSLRL). It enhanced the classification performance of traditional LRR-Graph based SSL by 5–30% and the running time is reduced from hundreds to less than ten seconds. Based on this method, a new outlier detection strategy is presented. This strategy succeeds with an AUC of at least 93% even if the detection condition of LRR is not satisfied. The effectiveness of SSLRL is demonstrated in semi-supervised classification, outlier detection, and salient detection tasks. These extensive experimental results highlight the outperforming of our method over state-of-the-art methods.

An efficient method for learning nonlinear ranking SVM functions

The problem of learning ranking (or preference) functions has become important in recent years as various applications have been found in information retrieval. Among the rank learning methods, RankSVM has been favorably used in various applications, e.g., optimizing search engines and improving data retrieval quality. Fast learning methods for linear RankSVM (RankSVM with a linear kernel) have been extensively developed, whereas methods for nonlinear RankSVM (RankSVM with nonlinear kernels) are lacking. This paper proposes an efficient method for learning with nonlinear kernels, called Ranking Vector SVM (RV-SVM). RV-SVM utilizes training vectors rather than pairwise difference vectors to determine the support vectors, and is thus faster to train than conventional RankSVMs. Experimental comparisons with the state-of-the-art RankSVM implementation provided in SVM-light show that RV-SVM is substantially faster for nonlinear kernels, although our method is slower for linear kernels. RV-SVM also uses far fewer support vectors, and thus the trained models are much simpler than those built by RankSVMs while maintaining comparable accuracy. Our implementation of RV-SVM is accessible at http://dm.hwanjoyu.org/rv-svm.

RankingFor Web Databases Using SVM and K-Means algorithm

The Usage of internet in now a day is more and it became necessity for the people to do some applications such as searching web data bases in domains like Animation, vehicles, Movie, Real estates, etc. One of the problems in this context is ranking the results of a user query information. Earlier approaches problem have toused frequencies of database value regions, handling query logs, and user profiles information. A common thread in most of these approaches is that ranking is done in a usage page ranking manner. This paper simulates the usage of ranking query results based on user and query Dependent ranks by taking user and query similarities as input including the workload. K- Means algorithm used for cluster and re ranking process, multiple database system used for clustering the data. Among rank learning methods, ranking SVM has been favorably applied to various applications, e.g., optimizing search engine for web information, improving data retrieval quality. We define these similarities formally in discuss their effectiveness analytically and experimentally over four distinct web databases. General Terms -Clustering, Support vector machine, K-means algorithm, Web Databases.