Using affective parameters in a content-based recommender system for images

There is an increasing amount of multimedia content available to end users. Recommender systems help these end users by selecting a small but relevant subset of items for each user based on her/his preferences. This paper investigates the influence of affective metadata (metadata that describe the user’s emotions) on the performance of a content-based recommender (CBR) system for images. The underlying assumption is that affective parameters are more closely related to the user’s experience than generic metadata (e.g. genre) and are thus more suitable for separating the relevant items from the non-relevant. We propose a novel affective modeling approach based on users’ emotive responses. We performed a user-interaction session and compared the performance of the recommender system with affective versus generic metadata. The results of the statistical analysis showed that the proposed affective parameters yield a significant improvement in the performance of the recommender system.     

CinemaScreen recommender agent: combining collaborative and content-based filtering

A film recommender agent expands and fine-tunes collaborative-filtering results according to filtered content elements - namely, actors, directors, and genres. This approach supports recommendations for newly released, previously unrated titles. Directing users to relevant content is increasingly important in today's society with its ever-growing information mass. To this end, recommender systems have become a significant component of e-commerce systems and an interesting application domain for intelligent agent technology.     

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, using syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommending only items that bear strong resemblance to those the user already knows. Traditional collaborative approaches face up to overspecialization by considering the preferences of other users, which causes other severe limitations. In this paper, we avoid the intrinsic pitfalls of collaborative solutions and diversify the recommendations by reasoning about the semantics of the user’s preferences. Specifically, we present a novel content-based recommendation strategy that resorts to semantic reasoning mechanisms adopted in the Semantic Web, such as Spreading Activation techniques and semantic associations. We have adopted these mechanisms to fulfill the personalization requirements of recommender systems, enabling to discover extra knowledge about the user’s preferences and leading to more accurate and diverse suggestions. Our approach is generic enough to be used in a wide variety of domains and recommender systems. The proposal has been preliminary evaluated by statistics-driven tests involving real users in the recommendation of Digital TV contents. The results reveal the users’ satisfaction regarding the accuracy and diversity of the reasoning-driven content-based recommendations.     

The effects of transparency on trust in and acceptance of a content-based art recommender

The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study’s results.

A multi-agent legal recommender system

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, the detailed design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology.     

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.     

资源自适应的实时新闻推荐系统

为解决新闻推荐系统性能差、效率低等问题,更好地满足商业应用的需要,设计了基于内容的资源自适应实时新闻推荐系统EagleNews.该系统自动监控系统负载情况,通过自动调整被推荐新闻集合的时间窗口,控制新闻数量,调整文档向量和用户模型向量的维度,优化相似度计算速度,提高系统性能,同时兼顾了推荐效果.最后,在原型系统上对提出的方法进行了评测,获得了系统运行的最佳参数,表明该系统不仅具有良好的性能,同时能够提供较好的推荐效果.     

Argumentation-enabled interest-based personalised recommender system

Recommender systems (RSs) use information filtering to recommend information of interest (to a user). Similarly, personalisation can be adopted for recommendations in e-market. We propose a new and innovative system called as interest-based recommender system (IBRS) for personalisation of recommendations. The IBRS is an agent-based RS that takes into account user's preferences. It can transform a standard product (or service) into a dedicated solution for an individual. The system discovers interesting product alternatives based on user's underlying mental attitudes (likes and dislikes) during the repair process using argumentation. The proposed method combines a hybrid RS approach with automated argumentation-based reasoning between agents. The system improves results by improving the recommendation repair activity. We consider a book recommendation application, for experiment to carry out the system's (objective and subjective) evaluation using standard metrics. The experiments confirm that the proposed IBRS improves user's acceptance of the product as compared with a traditional hybrid method and an argumentation-enabled state-of-the-art recommendation method. The system has been found to be more effective than its traditional counterpart when dealing with the new user problems.     

A time-aware spatio-textual recommender system

Location-Based Social Networks (LBSNs) allow users to post ratings and reviews and to notify friends of these posts. Several models have been proposed for Point-of-Interest (POI) recommendation that use explicit (i.e. ratings, comments) or implicit (i.e. statistical scores, views, and user influence) information. However the models so far fail to capture sufficiently user preferences as they change spatially and temporally. We argue that time is a crucial factor because user check-in behavior might be periodic and time dependent, e.g. check-in near work in the mornings and check-in close to home in the evenings. In this paper, we present two novel unified models that provide review and POI recommendations and consider simultaneously the spatial, textual and temporal factors. In particular, the first model provides review recommendations by incorporating into the same unified framework the spatial influence of the users’ reviews and the textual influence of the reviews. The second model provides POI recommendations by combining the spatial influence of the users’ check-in history and the social influence of the users’ reviews into another unified framework. Furthermore, for both models we consider the temporal dimension and measure the impact of time on various time intervals. We evaluate the performance of our models against 10 other methods in terms of precision and recall. The results indicate that our models outperform the other methods.     

Bounded matrix factorization for recommender system

Matrix factorization has been widely utilized as a latent factor model for solving the recommender system problem using collaborative filtering. For a recommender system, all the ratings in the rating matrix are bounded within a pre-determined range. In this paper, we propose a new improved matrix factorization approach for such a rating matrix, called Bounded Matrix Factorization (BMF), which imposes a lower and an upper bound on every estimated missing element of the rating matrix. We present an efficient algorithm to solve BMF based on the block coordinate descent method. We show that our algorithm is scalable for large matrices with missing elements on multicore systems with low memory. We present substantial experimental results illustrating that the proposed method outperforms the state of the art algorithms for recommender system such as stochastic gradient descent, alternating least squares with regularization, SVD++ and Bias-SVD on real-world datasets such as Jester, Movielens, Book crossing, Online dating and Netflix.     

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.     

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...     

A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.     

User evaluation of a market-based recommender system

Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommender.     

Hybrid immunizing solution for job recommender system

Two traditional recommendation techniques, content-based and collaborative filtering (CF), have been widely used in a broad range of domain areas. Both methods have their advantages and disadvantages, and some of the defects can be resolved by integrating both techniques in a hybrid model to improve the quality of the recommendation. In this article, we will present a problem-oriented approach to design a hybrid immunizing solution for job recommendation problem from applicant’s perspective. The proposed approach aims to recommend the best chances of opening jobs to the applicant who searches for job. It combines the artificial immune system (AIS), which has a powerful exploration capability in polynomial time, with the collaborative filtering, which can exploit the neighbors’ interests. We will discuss the design issues, as well as the hybridization process that should be applied to the problem. Finally, experimental studies are conducted and the results show the importance of our approach for solving the job recommendation problem.     

Visual information retrieval system via content-based approach

In this paper a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content is proposed. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. A new indexing method that supports fast retrieval in large image databases is also presented. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.2 percent of the images from direct comparison.     

基于深度学习的矩阵分解推荐模型

通过对深度学习和矩阵分解技术进行结合,设计一个深度神经网络对用户和物品进行特征提取,形成用户隐向量和物品隐向量的方法,计算这两个隐向量的内积得到用户对物品的评分预测.为提高推荐精度,提出使用显式数据和隐式数据并设计新的损失函数能够同时计算这两类数据损失的方法.在两个公开数据集上的实验结果表明,该方法比基线模型在HR和N...     

An efficient recommender system algorithm using trust data

The Journal of Supercomputing - Smart services are a concept that provides services to the citizens in an efficient manner. The online shopping and recommender system can play an important role for...     

基于CBR和MADM的多Agent推荐系统

为解决目前基于CBR的推荐系统只考虑属性值全部为精确或全部为非精确数据的情况,提出一种基于MADM的多Agent推荐系统框架。在考虑了属性分类的基础上设计了基于距离的混合数据类型的相似性度量算法及TOPSIS多属性决策方法,设计了该系统各组成部分功能、结构和流程。模拟算例演示了案例推理及多属性决策在本系统的应用过程,结果表明该系统有较好的实用性。