基于领域本体的语义查询扩展

在信息检索研究领域,资源与查询词的匹配决定信息检索质量。现有检索方法的检索结果存在过多不相关信息,不能很好满足用户需求。针对传统信息检索存在的问题与当前语义查询扩展方法的特点,本文在分析各种语义查询扩展方法及其相关研究的基础上,提出一种改进的基于领域本体的语义查询扩展方法。该方法论通过本体模型和概念相似度的计算对检索信息进行检索意图树的构建并扩展;然后在资源本体中以最短路径的方式搜索资源。实验结果表明,本文方法相较其他查询扩展方法能得到更好的检索结果。     

Exploiting rich user information for one-class collaborative filtering

One-class collaborative filtering (OCCF) is an emerging setup in collaborative filtering in which only positive examples or implicit feedback can be observed. Compared with the traditional collaborative filtering setting where the data have ratings, OCCF is more realistic in many scenarios when no ratings are available. In this paper, we propose to improve OCCF accuracy by exploiting the rich user information that is often naturally available in community-based interactive information systems, including a user’s search query history, and purchasing and browsing activities. We propose two major strategies to incorporate such user information into the OCCF models: One is to linearly combine scores from different sources, and the other is to embed user information into collaborative filtering. Furthermore, we employ the MapReduce framework for similarity computation over millions of users and items. Experimental results on two large-scale retail datasets from a major e-commerce company show that the proposed methods are effective and can improve the performance of the OCCF over baseline methods through leveraging rich user information.     

基于多上下文信息的协同过滤推荐算法

随着电子商务和互联网的发展,数据信息呈爆炸式增长,协同过滤算法作为一种简单而高效的推荐算法,能在一定程度上有效地解决信息爆炸问题.但是传统协同过滤算法仅通过单一评分来挖掘相似用户,推荐效果并不占优势.为了提高个性化推荐的质量,如何充分利用用户(物品)的文本、图片、标签等上下文信息以使数据价值最大化是当前推荐系统亟待解决...     

TAFFY: incorporating tag information into a diffusion process for personalized recommendations

The last few years have witnessed an explosion of information caused by the exponential growth of the Internet and World Wide Web, which confronted us with information overload and brought about an era of big data, appealing for efficient personalized recommender systems to assist the screening of useful information from various sources. As for a recommender system with more than the fundamental object-user rating information, such accessorial information as tags can be exploited and integrated into final ranking lists to improve recommendation performance. However, although existing studies have demonstrated that tags, as the additional yet useful resource, can be designed to improve recommendation performance, most network-based approaches take users, objects and tags as two bipartite graphs, or a tripartite graph, and therefore overlook either the important information among homogeneous nodes in each sub-graph, or the bipartite relations between users, objects or tags. Moreover, recent studies have suggested that the filtration of weak relationships in networks may reasonably enhance recommendation performance of collaborative filtering methods, and it has also been demonstrated that approaches based on the diffusion processes could more effectively capture relationships between objects and users, hence exhibiting higher performance than a typical collaborative filtering method. Based on these understandings, we propose a data fusion approach that integrates historical and tag data towards personalized recommendations. Our method coverts historical and tag data into complex networks, resorts to a diffusion kernel to measure the strength of associations between users and objects, and adopts Fisher’s combined probability test to obtain the statistical significance of such associations for personalized recommendations. We validate our approach via 10-fold cross-validation experiments. Results show that our method outperforms existing methods in not only the recommendation accuracy and diversity, but also retrieval performance. We further show the robustness of our method to related parameters.     

一种融合项目特征和移动用户信任关系的推荐算法

协同过滤推荐系统中普遍存在评分数据稀疏问题.传统的协同过滤推荐系统中的余弦、Pearson 等方法都是基于共同评分项目来计算用户间的相似度;而在稀疏的评分数据中,用户间共同评分的项目所占比重较小,不能准确地找到偏好相似的用户,从而影响协同过滤推荐的准确度.为了改变基于共同评分项目的用户相似度计算,使用推土机距离（earth mover's distance,简称EMD）实现跨项目的移动用户相似度计算,提出了一种融合项目特征和移动用户信任关系的协同过滤推荐算法.实验结果表明：与余弦、Pearson 方法相比,融合项目特征的用户相似度计算方法能够缓解评分数据稀疏对协同过滤算法的影响.所提出的推荐算法能够提高移动推荐的准确度.     

Multi-domain collaborative exploration mechanisms for query expansion in an agent-based filtering framework

Novice users often do not have enough domain knowledge to create good queries for searching information on-line. To help alleviate the situation, exploration techniques have been used to increase the diversity of the search results so that not only those explicitly asked will be returned, but also those potentially relevant ones will be returned too. Most existing approaches, such as collaborative filtering, do not allow the level of exploration to be controlled. Consequently, the search results can be very different from what is expected. We propose an exploration strategy that performs intelligent query processing by first searching usable old queries, and then utilising them to adapt the current query, with the hope that the adapted query will be more relevant to the user’s areas of interest. We applied the proposed strategy to the implementation of a personal information assistant (PIA) set up for user evaluation for 3 months. The experimental results showed that the proposed exploration method outperformed collaborative filtering, and mutation and crossover methods by around 25% in terms of the elimination of off-topic results.     

基于多分段改进PCC的协同过滤算法

随着互联网的快速发展,大量各式各样的信息呈爆发式增长,导致了信息过载。如今,推荐系统可以通过分析大量的可用信息帮助用户找到他们感兴趣的对象。其中,协同过滤算法是推荐系统中使用得最广泛的推荐算法。但是,协同过滤推荐算法在推荐的准确度上还有待改进。提出了一种基于多分段改进PCC的协同过滤推荐算法,用于提高推荐系统的准确度。提出的方法将根据用户公共项目数和PCC阈值,对PCC算法进行分段计算并改进结果。最后的实验结果表明,该方法的推荐效果要优于其他传统的推荐方法。     

Multi-domain collaborative exploration mechanisms for query expansion in an agent-based filtering framework

Novice users often do not have enough domain knowledge to create good queries for searching information on-line. To help alleviate the situation, exploration techniques have been used to increase the diversity of the search results so that not only those explicitly asked will be returned, but also those potentially relevant ones will be returned too. Most existing approaches, such as collaborative filtering, do not allow the level of exploration to be controlled. Consequently, the search results can be very different from what is expected. We propose an exploration strategy that performs intelligent query processing by first searching usable old queries, and then utilising them to adapt the current query, with the hope that the adapted query will be more relevant to the user’s areas of interest. We applied the proposed strategy to the implementation of a personal information assistant (PIA) set up for user evaluation for 3 months. The experimental results showed that the proposed exploration method outperformed collaborative filtering, and mutation and crossover methods by around 25% in terms of the elimination of off-topic results.     

基于用户潜在偏好的协同过滤

提出了一种新的协同过滤模型,解决了不同用户在项目上,有相似的偏好、不同的评分习惯的问题。该模型可有效地改进传统协同过滤模型相似性度量方法,提高了用户相似性度量准确性。实验结果表明,该模型在个性化推荐系统应用中取得了较好的效果。     

Improving sparsity and new user problems in collaborative filtering by clustering the personality factors

In collaborative filtering recommender systems, items recommended to an active user are selected based on the interests of users similar to him/her. Collaborative filtering systems suffer from the ‘sparsity’ and ‘new user’ problems. The former refers to the insufficiency of data about users’ preferences and the latter addresses the lack of enough information about the new-coming user. Clustering users is an effective way to improve the performance of collaborative filtering systems in facing the aforementioned problems. In previous studies, users were clustered based on characteristics such as ratings given by them as well as their age, gender, occupation, and geographical location. On the other hand, studies show that there is a significant relationship between users’ personality traits and their interests. To alleviate the sparsity and new user problems, this paper presents a new collaborative filtering system in which users are clustered based on their ‘personality traits’. In the proposed method, the personality of each user is described according to the big-5 personality model and users with similar personality are placed in the same cluster using K-means algorithm. The unknown ratings of the sparse user-item matrix are then estimated based on the clustered users, and recommendations are found for a new user according to a user-based approach which relays on the interests of the users with similar personality to him/her. In addition, for an existing user in the system, recommendations are offered in an item-based approach in which the similarity of items is estimated based on the ratings of users similar to him/her in personality. The proposed method is compared to some former collaborative filtering systems. The results demonstrate that in facing the data sparsity and new user problems, this method reduces the mean absolute error and improves the precision of the recommendations.     

基于兴趣度的协作过滤技术研究

随着互联网上的信息的迅速增长,协作过滤技术得到越来越广泛的应用。结合了显式和隐式计算兴趣度的方法,提出了一种新的计算用户兴趣度的方法。并在此基础上论述了基于兴趣度的协作过滤技术。该方法通过寻找相似用户群体,由相似用户群体来预测用户对某一WEB事务的喜好,并给出了相应的实现算法。实验结果表明,该方法能提供较好的协作推荐服务。     

基于社交网络能量扩散的协同过滤推荐算法

针对数据稀疏导致推荐系统精确度较低的问题,结合社交网络中丰富的社会化信息及能量扩散在数据稀疏问题上的优良表现,文中提出基于社交网络能量扩散的协同过滤推荐算法.首先利用用户-物品评分矩阵和信任关系具有的传递性计算用户之间信任强度值.再利用社交网络结合用户-物品二分网络,得到物品资源值.最后利用协同过滤方法进行预测评分.在真实数据集上的实验表明,文中算法缓解数据稀疏性,可解决推荐精确度较低的问题.     

基于共同用户和相似标签的好友推荐方法

针对目前的社交网络好友推荐方法用户兴趣不明显、用户之间相关性较差等问题,提出一种基于共同用户和相似标签的协同过滤算法。抽取共同关注用户作为共同项目,加入体现用户兴趣的自定义标签数据,并对标签进行相似度计算处理,以扩充稀疏矩阵,改善协同过滤推荐方法。实验结果表明,与单指标的协同过滤推荐算法相比,基于共同用户和相似标签的好友推荐方法更好地体现了用户兴趣,同时在推荐准确率和平均准确率上都有较大提高。     

基于Jaccard相似度和位置行为的协同过滤推荐算法

协同过滤是现今推荐系统中应用最为成功且最广泛的推荐方法之一,其中概率矩阵分解算法作为一类重要的协同过滤方式,能够通过学习低维的近似矩阵进行推荐。然而,传统的协同过滤推荐算法在推荐过程中只利用用户-项目评分信息,忽略了用户(项目)间的潜在影响力,影响了推荐精度。针对上述问题,首先利用Jaccard相似度对用户(项目)做预处理,而后通过用户(项目)间的位置信息挖掘出其间的潜在影响力,成功找到最近邻居集合;最后将该邻居集合融合到基于概率矩阵分解的协同过滤推荐算法中。实验证明该算法较传统的协同过滤推荐算法能够更有效地预测用户的实际评分,提高了推荐效果。     

A collaborative filtering framework based on both local user similarity and global user similarity

Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other state-of-the-art collaborative filtering algorithms.     

协同过滤推荐系统综述

随着互联网和信息计算的飞速发展,衍生了海量数据,我们已经进入信息爆炸的时代。网络中各种信息量的指数型增长导致用户想要从大量信息中找到自己需要的信息变得越来越困难,信息过载问题日益突出。推荐系统在缓解信息过载问题中起着非常重要的作用,该方法通过研究用户的兴趣偏好进行个性化计算,由系统发现用户兴趣进而引导用户发现自己的信息需求。目前,推荐系统已经成为产业界和学术界关注、研究的热点问题,应用领域十分广泛。在电子商务、会话推荐、文章推荐、智慧医疗等多个领域都有所应用。传统的推荐算法主要包括基于内容的推荐、协同过滤推荐以及混合推荐。其中,协同过滤推荐是推荐系统中应用最广泛最成功的技术之一。该方法利用用户或物品间的相似度以及历史行为数据对目标用户进行推荐,因此存在用户冷启动和项目冷启动问题。此外,随着信息量的急剧增长,传统协同过滤推荐系统面对数据的快速增长会遇到严重的数据稀疏性问题以及可扩展性问题。为了缓解甚至解决这些问题,推荐系统研究人员进行了大量的工作。近年来,为了提高推荐效果、提升用户满意度,学者们开始关注推荐系统的多样性问题以及可解释性等问题。由于深度学习方法可以通过发现数据中用户和项目之间的非线性关系从而学习一个有效的特征表示,因此越来越受到推荐系统研究人员的关注。目前的工作主要是利用评分数据、社交网络信息以及其他领域信息等辅助信息,结合深度学习、数据挖掘等技术提高推荐效果、提升用户满意度。对此,本文首先对推荐系统以及传统推荐算法进行概述,然后重点介绍协同过滤推荐算法的相关工作。包括协同过滤推荐算法的任务、评价指标、常用数据集以及学者们在解决协同过滤算法存在的问题时所做的工作以及努力。最后提出未来的几个可研究方向。     

OHR:一种基于本体的个性化混合服务推荐模型

随着网络信息量的日益增加,为用户提供个性化服务是一种趋势。该文通过建立一个通用的服务本体模型,将项目集合划分到多个服务子类中,经过概率计算得到用户的兴趣分布,并在此基础上提出了一个结合内容过滤和项目协同过滤的个性化混合服务推荐模型(OHR)。实验结果表明了该模型在服务推荐上具有较高的准确率和发现用户新兴趣的能力。     

使用群体兴趣偏好度的协同过滤推荐

推荐系统是电子商务系统中最重要的技术之一,用户相似性度量方法是影响推荐算法准确率高低的关键因素。针对用户评分数据极端稀疏情况下传统相似性度量方法的不足,提出了一种基于群体兴趣偏好度的协同过滤推荐算法,根据群体兴趣偏好度来预测用户对未评分项目的评分,在此基础上再采用传统的相似性度量方法计算目标用户的最近邻居。实验结果表明,该算法可以有效解决用户评分数据极端稀疏情况下传统相似性度量方法存在的问题,显著提高推荐系统的推荐质量。     

基于社交用户标签的混合top-N推荐方法

针对协同过滤方法的冷启动问题,提出一种将社交用户标签与协同过滤相结合的混合top-N推荐方法。通过社交用户关系获得可信用户集,然后根据个性化标签采用结构上下文相似性算法(SimRank)计算社交用户相似近邻集并进行预测推荐,最后结合传统协同过滤方法进行推荐。实验结果表明,该方法能够提高在一般数据集及冷启动用户数据集下的推荐精度。