基于多模信息融合的个性化推荐系统的研究

为了解决信息过载的问题,我们可以通过在用户和产品之间建立二元关系的方法,利用已经拥有的比较相似的关系或者选择过程,挖掘出各用户可能感兴趣的对像。目前解决信息过载问题最有效的工具就是个性化推荐,该文利用不同的推荐算法,简单介绍了协同过滤系统,基于内容的推荐系统,基于用户—产品二部图网络结构的推荐系统,混合推荐系统。并分析这些推荐系统的特点以及存在的缺陷,帮助读者了解这个研究领域。     

面向微博系统的实时个性化推荐

社交网络服务需要响应用户实时、连续、个性化的服务需求.然而,目前多数社交网络服务并未充分考虑用户的个性化服务需求.由于社交网络中海量的数据更新使得提供实时个性化服务成为一项相对艰巨的任务.文中基于LDA主题模型推断微博的主题分布和用户的兴趣取向,提出了微博系统上用户感兴趣微博的实时推荐方法,以响应用户实时、连续和个性化的服务请求,在真实数据集上的实验结果验证了文中提出的方法的有效性和高效性.     

个性化学习推荐系统的设计与实现

针对远程教学系统中的个性化服务需求，本文在介绍个性化服务相关技术的基础上，提出了基于内容过滤和协同过滤两种方法相结合的个性化推荐算法，设计并实现了个性化学习推荐系统。     

基于隐性反馈的自适应推荐系统研究

介绍个性化自适应推荐系统的整体架构与设计方法。阐述用户兴趣模型的建立,包括对用户个性化信息的收集、精炼处理、模糊语意处理、解模糊化及满意度计算。引入模糊自适应共振理论网络进行项目聚类分析,并进行推荐处理,实现自适应推荐服务。实验结果表明,系统对用户兴趣判断比较准确,能及时掌握用户兴趣偏移,推荐效果良好,且基本稳定。     

基于自适应滤波的个性化推荐系统设计

电子商务发展迅速,互联网的发展更成为电子商务的引擎,电子商务网站如雨后春笋一样成长起来,但是"信息超载"成为当前电子商务网站的一大弊端,让客户面对纷繁复杂的货物一筹莫展。该文分析了当前应用的几种个性化推荐方法,对它们的不足进行了分析,并提出了一种基于自适应滤波的个性化推荐系统,能为客户提供个性化的推荐服务,而且随着时间的推移,系统在性能上会更加出众。     

基于学习风格的个性化自适应资源推荐算法研究

个性化自适应资源推荐是以学习者为中心、以人工智能和大数据技术为基础,模拟人类思维进行学习资源推荐的过程。论文在分析学习者和资源学习风格的基础上,分别构建学习者模型和资源模型,运用基于学习风格过滤推荐算法、协同过滤推荐算法、关联规则推荐算法,展开个性化自适应资源推荐研究。研究结果表明,以学习风格为基础的混合式自适应推荐的结果,更贴合学习者的个性化学习需求。     

基于Spark的实时广告推荐系统研究

随着信息技术及智能移动设备的发展和普及,广告的推送方式和投放平台呈现多样化。传统电商推荐系统的运行速度较慢,无法根据根据用户的实际需求进行推荐。实时广告推荐系统作为应对这些挑战的有效手段,成为个性化服务领域的研究热点之一。文章重点分析了基于Spark的实时广告推荐系统,以期为相关研究提供借鉴。     

融合的协同过滤推荐算法

协同过滤算法作为一种成功的个性化推荐技术已经被应用到很多领域中,但随着系统规模的扩大,它的效率逐渐降低。针对它出现的缺点,提出一种新的基于内容和网络结构图的混合算法,实验数据证明该算法可以解决传统推荐算法中存在的一些缺陷。     

一种适合于保险产品的个性化推荐算法

随着保险电子商务的不断发展,保险网站的用户越来越多样化,需求差异越来越大,为不同类型的用户推荐个性化定制化的产品以提高网站销量已经成为行业趋势。针对该问题,提出基于保险行业电子商务网站的个性化推荐系统。系统采用了基于内容的推荐和基于关联规则的推荐,分别利用保险产品本身的分类特点和用户访问网站的历史记录来推荐产品,最后将两种算法进行组合推荐。实验结果表明,算法性能高,平均推荐准确率在8%左右。由此得出结论,所提算法可用于网站的线上预测推荐。     

MONERS: A news recommender for the mobile web

Mobile web news services, which served by mobile service operators collecting news articles from diverse news contents providers, provide articles sorted by category or on the basis of attributes, such as the time at which they were posted. The mobile web should provide easy access to the categories or news contents preferred by users because user interface of wireless devices, particularly cell phones is limited for browsing between contents.This paper presents a mobile web news recommendation system (MONERS) that incorporates news article attributes and user preferences with regard to categories and news articles. User preference of news articles are estimated by aggregating news article importance and recency, user preference change, and user segment’s preference on news categories and articles. Performance of MONERS was tested in an actual mobile web environment; news organized by category had more page hits, while recommended news had a higher overall article read ratio.     

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.     

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.     

多Agent协同的电子商务推荐系统模型

为了进一步提高电子商务推荐系统中商品推荐的准确性和高效性,通过分析传统推荐系统存在的问题和已有的优化方案,提出了多Agent的电子商务推荐系统模型。推荐系统通过人工智能领域中的多Agent技术,并应用终端自适应特性,改善了传统推荐系统在多终端情况下的电子商务系统的推荐效率,并根据用户使用的不同终端动态返回推荐结果。实验结果表明,多Agent协同的电子商务推荐系统在一定程度上提高了推荐效率和准确性。     

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.     

In-memory,distributed content-based recommender system

Burdened by their popularity, recommender systems increasingly take on larger datasets while they are expected to deliver high quality results within reasonable time. To meet these ever growing requirements, industrial recommender systems often turn to parallel hardware and distributed computing. While the MapReduce paradigm is generally accepted for massive parallel data processing, it often entails complex algorithm reorganization and suboptimal efficiency because mid-computation values are typically read from and written to hard disk. This work implements an in-memory, content-based recommendation algorithm and shows how it can be parallelized and efficiently distributed across many homogeneous machines in a distributed-memory environment. By focusing on data parallelism and carefully constructing the definition of work in the context of recommender systems, we are able to partition the complete calculation process into any number of independent and equally sized jobs. An empirically validated performance model is developed to predict parallel speedup and promises high efficiencies for realistic hardware configurations. For the MovieLens 10 M dataset we note efficiency values up to 71 % for a configuration of 200 computing nodes (eight cores per node).     

Affective recommender systems in online news industry: how emotions influence reading choices

Recommender systems have become ubiquitous over the last decade, providing users with personalized search results, video streams, news excerpts, and purchasing hints. Human emotions are widely regarded as important predictors of behavior and preference. They are a crucial factor in decision making, but until recently, relatively little has been known about the effectiveness of using human emotions in personalizing real-world recommender systems. In this paper we introduce the Emotion Aware Recommender System (EARS), a large scale system for recommending news items using user’s self-assessed emotional reactions. Our original contribution includes the formulation of a multi-dimensional model of emotions for news item recommendations, introduction of affective item features that can be used to describe recommended items, construction of affective similarity measures, and validation of the EARS on a large corpus of real-world Web traffic. We collect over 13,000,000 page views from 2,700,000 unique users of two news sites and we gather over 160,000 emotional reactions to 85,000 news articles. We discover that incorporating pleasant emotions into collaborative filtering recommendations consistently outperforms all other algorithms. We also find that targeting recommendations by selected emotional reactions presents a promising direction for further research. As an additional contribution we share our experiences in designing and developing a real-world emotion-based recommendation engine, pointing to various challenges posed by the practical aspects of deploying emotion-based recommenders.

     

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.