A content-based goods image recommendation system

The information of e-commerce images varies and different users may focus on different contents of the same image for different purpose. So the research on recommendation by computers is becoming more and more important. But retrieval based only on keywords obviously falls short for massive numbers of resource images. In this paper, we focus on a recommendation system of goods images based on image content. Goods images have a relatively homogenous background and have a wide range of applications. The recommendation consists of three stages. First, the image is pre-processed by removing the background. Second, a weighted representation model is proposed to represent the image. The separated features are extracted and normalized, and then the weights of each feature are computed based on the samples browsed by the users. Third, a feature indexing scheme is put forward based on the proposed representation. A binary-tree is used for the indexing, and a binary-tree updating algorithm is also given. Finally, the recommended images are given by a features combination searching scheme. Experimental results on a real goods image database show that our algorithm can achieve high accuracy in recommending similar goods images with high speed.     

An effective content-based event recommendation model

Event-based social networks (EBSNs) facilitate people to interact with each other by sharing similar interests in online groups or taking part in offline events together. Event recommendation in EBSNs has been studied by many researchers. However, the problem of recommending the event to the top N active-friends of the key user has rarely been studied in EBSNs. In this paper, we propose a new method to solve this problem. In this method, we first construct an association matrix from the content of events and user features. Then, we define a new content-based event recommendation model, which combines the matrix, spatio-temporal relations and user interests to recommend an event to the active-friends of a key user. A series of experiments were conducted on real datasets collected from Meetup, and the comparison results have demonstrated the effectiveness of the new model.

     

A content-based recommendation algorithm for learning resources

Automatic multimedia learning resources recommendation has become an increasingly relevant problem: it allows students to discover new learning resources that match their tastes, and enables the e-learning system to target the learning resources to the right students. In this paper, we propose a content-based recommendation algorithm based on convolutional neural network (CNN). The CNN can be used to predict the latent factors from the text information of the multimedia resources. To train the CNN, its input and output should first be solved. For its input, the language model is used. For its output, we propose the latent factor model, which is regularized by L₁-norm. Furthermore, the split Bregman iteration method is introduced to solve the model. The major novelty of the proposed recommendation algorithm is that the text information is used directly to make the content-based recommendation without tagging. Experimental results on public databases in terms of quantitative assessment show significant improvements over conventional methods. In addition, the split Bregman iteration method which is introduced to solve the model can greatly improve the training efficiency.     

基于内容的推荐与协同过滤融合的新闻推荐方法

针对基于内容的新闻推荐方法中用户兴趣多样性的缺乏问题和混合推荐方法存在的冷启动问题,提出一种基于内容与协同过滤融合的方法进行新闻推荐。首先利用基于内容的方法发现用户既有兴趣;再用内容与行为的混合相似度模式,寻找目标用户的相似用户群,预测用户对特征词的兴趣度,发现用户潜在兴趣;然后将用户既有兴趣与潜在兴趣融合,得到兼具个性化和多样性的用户兴趣模型;最后将候选新闻与融合模型进行相似度计算,形成推荐列表。实验结果显示,与基于内容的推荐方法相比,所提方法的F-measure和整体多样性Diversity均有明显提高;与混合推荐方法相比,性能相当,但候选新闻无需耗时积累足够的用户点击量,不存在冷启动问题。     

基于Vague集的内容推荐算法研究

针对内容推荐系统中商品特征表示的困难,将Vague集方法引入到这一领域,并利用高斯函数和Vague集中未知度的一些性质,将商品的特征用Vague值形象地表示;在此基础上给出了运用Vague集方法进行内容推荐的一般步骤,为推荐系统的设计者提供了一种新的指导思想和方法;最后给出了多个推荐公式,并对各式进行了简单的比较,这将有利于实际推荐工作中酌情选择不同的推荐公式,提高商品推荐质量和推荐精度。     

Solving distribution problems in content-based recommendation system with gaussian mixture model

Applied Intelligence - Recommendation systems play an important role in boosting purchasing consumption for many manufacturers by helping consumers find the most appropriate items. Furthermore,...     

Comparison of content-based music recommendation using different distance estimation methods

With rapid growth in the online music market, music recommendation has become an active research area. In most current approaches, content-based recommendation methods play an important role. Estimation of similarity between music content is the key to these approaches. A distance formula is used to calculate the music distance measure, and music recommendations are provided based on this measure. However, people have their own unique tastes in music. This paper proposes a method to calculate a personalized distance measure between different pieces of music based on user preferences. These methods utilize a randomized algorithm, a genetic algorithm, and genetic programming. The first two methods are based on Euclidean distance calculation, where the weight of each music feature in the distance calculation approximates user perception. The third method is not limited to Euclidean distance calculation. It generates a more complex distance function to estimate a user’s music preferences. Experiments were conducted to compare the distance functions calculated by the three methods, and to compare and evaluate their performance in music recommendation.     

Association and content-based retrieval

In spite of important efforts in content-based indexing and retrieval during these last years, seeking relevant and accurate images remains a very difficult query. In the state-of-the-art approaches, the retrieval task may be efficient for some queries in which the semantic content of the query can be easily translated into visual features. For example, finding images of fires is simple because fires are characterized by specific colors (yellow and red). However, it is not efficient in other application fields in which the semantic content of the query is not easily translated into visual features. For example, finding images of birds during migrations is not easy because the system has to understand the query semantic. In the query, the basic visual features may be useful (a bird is characterized by a texture and a color), but they are not sufficient. What is missing is the generalization capability. Birds during migrations belong to the same repository of birds, so they share common associations among basic features (e.g., textures and colors) that the user cannot specify explicitly. We present an approach that discovers hidden associations among features during image indexing. These associations discriminate image repositories. The best associations are selected on the basis of measures of confidence. To reduce the combinatory explosion of associations, because images of the database contain very large numbers of colors and textures, we consider a visual dictionary that group together similar colors and textures.     

Localized content-based image retrieval

We define localized content-based image retrieval as a CBIR task where the user is only interested in a portion of the image, and the rest of the image is irrelevant. In this paper we present a localized CBIR system, Accio, that uses labeled images in conjunction with a multiple-instance learning algorithm to first identify the desired object and weight the features accordingly, and then to rank images in the database using a similarity measure that is based upon only the relevant portions of the image. A challenge for localized CBIR is how to represent the image to capture the content. We present and compare two novel image representations, which extend traditional segmentation-based and salient point-based techniques respectively, to capture content in a localized CBIR setting.     

Learning similarity matching in multimedia content-based retrieval

Many multimedia content-based retrieval systems allow query formulation with the user setting the relative importance of features (e.g., color, texture, shape, etc.) to mimic the user's perception of similarity. However, the systems do not modify their similarity matching functions, which are defined during the system development. We present a neural network-based learning algorithm for adapting the similarity matching function toward the user's query preference based on his/her relevance feedback. The relevance feedback is given as ranking errors (misranks) between the retrieved and desired lists of multimedia objects. The algorithm is demonstrated for facial image retrieval using the NIST Mugshot Identification Database with encouraging results     

Adaptive content-based music retrieval system

This paper presents a tunable content-based music retrieval (CBMR) system suitable the for retrieval of music audio clips. The audio clips are represented as extracted feature vectors. The CBMR system is expert-tunable by altering the feature space. The feature space is tuned according to the expert-specified similarity criteria expressed in terms of clusters of similar audio clips. The main goal of tuning the feature space is to improve retrieval performance, since some features may have more impact on perceived similarity than others. The tuning process utilizes our genetic algorithm. The R-tree index for efficient retrieval of audio clips is based on the clustering of feature vectors. For each cluster a minimal bounding rectangle (MBR) is formed, thus providing objects for indexing. Inserting new nodes into the R-tree is efficiently performed because of the chosen Quadratic Split algorithm. Our CBMR system implements the point query and the n-nearest neighbors query with the O(logn) time complexity. Different objective functions based on cluster similarity and dissimilarity measures are used for the genetic algorithm. We have found that all of them have similar impact on the retrieval performance in terms of precision and recall. The paper includes experimental results in measuring retrieval performance, reporting significant improvement over the untuned feature space.     

Using relevance feedback in content-based image metasearch

MetaSeek is an image metasearch engine developed to explore the querying of large, distributed, online visual information systems. The current implementation integrates user feedback into a performance-ranking mechanism. MetaSeek selects and queries the target image search engines according to their success under similar query conditions in previous searches. The current implementation keeps track of each target engine's performance by integrating user feedback for each visual query into a performance database. We begin with a review of the issues in content-based visual query, then describe the current MetaSeek implementation. We present the results of experiments that evaluated the implementation in comparison to a previous version of the system and a baseline engine that randomly selects the individual search engines to query. We conclude by summarizing open issues for future research     

Qualitative part-based models in content-based image retrieval

A qualitative, volumetric part-based model is proposed to improve the categorical invariance and viewpoint invariance in content-based image retrieval, and a novel two-step part-categorization method is presented to build it. The method consists first in transforming parts extracted from a segmented contour primitive map and then categorizing the transformed parts using interpretation rules. The first step allows noisy extracted parts to be transformed to the domain of a simple classifier. The second step computes features of the transformed parts for categorization. Content-based image retrieval experiments using real images of complex multi-part objects confirm that a model built from the categorized parts improves both the categorical invariance and the viewpoint invariance. It does so by directly addressing the fundamental limits of low-level models.     

基于内容识别的Web集群负载均衡算法的研究

可扩展Web服务器集群是目前高性能网络服务器的主要架构方法,负载均衡技术是集群系统中任务分配的核心环节.提出了一种基于内容识别的负载均衡算法,引入了访问量阈值的概念,并通过动态的修正访问量阈值以适应网络负载的变化;利用动态反馈机制来获取服务器的负载状态,同时通过保证负载的局部性,减少相同内容在多个服务器中的重复缓存,提高服务器Cache的命中率.     

基于内容分发的Cache集群系统

文章设计和实现了大规模基于内容请求分发的系统。一个VS／DR或VS／TUN调度器作为系统的单一入口点，将请求分发给一组KTCPVS分发器，KTCPVS分发器通过使用中心的内容位置服务来确定处理该请求的后端服务器，然后，再将该请求分发到后端服务器，并提出基于局部性的最小负载调度算法。最后将该体系结构应用到大规模Cache集群系统中加以验证。     

Dynamic content-based adaptation of streamed multimedia

Most adaptive delivery mechanisms for streaming multimedia content do not explicitly consider user-perceived quality when making adaptation decisions. We show that an optimal adaptation trajectory (OAT) through the set of possible encodings exists, and that it indicates how to adapt encoding quality in response to changes in network conditions in order to maximise user-perceived quality. The OAT is related to the characteristics of the content, in terms of spatial and temporal complexity. We describe a method to automatically determine the OAT in response to the time-varying characteristics of the content. In this way, as the characteristics of the content change over time, the system can dynamically and intelligently adjust the adaptation process in order to maximise the user-perceived quality. The OAT can be used with any sender-based transmission adaptation policy. We demonstrate content-based adaptation using the OAT in a practical system using two different adaptation algorithms. Furthermore, we show how this form of adaptation can result in differing adaptation behaviour not only as a result of the dynamics of the content but also as a result of the adaptation algorithm being used. Finally, we show how increased feedback frequency does not necessarily improve the behaviour of the adaptation algorithm being used.     

A content-based deep intrusion detection system

International Journal of Information Security - The growing number of Internet users and the prevalence of web applications make it necessary to deal with very complex software and applications in...