Combining long-term and short-term user interest for personalized hashtag recommendation

Hashtags, terms prefixed by a hash-symbol #, are widely used and inserted anywhere within short messages (tweets) on micro-blogging systems as they present rich sentiment information on topics that people are interested in. In this paper, we focus on the problem of hashtag recommendation considering their personalized and temporal aspects. As far as we know, this is the first work addressing this issue specially to recommend personalized hashtags combining longterm and short-term user interest.We introduce three features to capture personal and temporal user interest: 1) hashtag textual information; 2) user behavior; and 3) time. We offer two recommendation models for comparison: a linearcombined model, and an enhanced session-based temporal graph (STG) model, Topic-STG, considering the features to learn user preferences and subsequently recommend personalized hashtags. Experiments on two real tweet datasets illustrate the effectiveness of the proposed models and algorithms.     

融合协同过滤与用户偏好的旅游组推荐方法

近年来,组推荐系统已经逐渐成为旅游推荐领域的研究热点之一。传统的推荐系统面临的数据稀疏性问题在组推荐系统中同样存在。基于评分的推荐系统中,可以把组推荐系统分为对单个用户的偏好预测和对组内成员预测结果的融合两个阶段。为提高推荐的效果,提出了一种融合协同过滤与用户偏好的旅游组推荐方法,它考虑了用户的预测评分和组推荐结果的准确性。在协同过滤中通过加入相似性影响因子和关联性因子进行预测评分,然后在均值策略和最小痛苦策略的基础上,提出了满意度平衡策略,该策略考虑了组内成员的局部满意度和整体满意度。实验表明,所提出的方法提高了推荐的准确率。     

结合信任机制和用户偏好的协同过滤推荐算法

仅凭相似度来定位邻居用户对传统协同过滤算法的性能有严重的负面影响。引入社会网络中的信任机制,从个体在社交圈中的主观信任和全局声誉角度出发建模。分别考虑用户交互、评分差和用户偏好调节生成直接信任度。利用声誉及专家信任优先模型聚合生成间接信任度,将两者动态加权形成用户之间的信任关系。用参数[η]协调信任和相似双属性,使用户关系更加紧密,有效地解决新用户和稀疏性问题。经实证,改良后的模型颇有成效。     

基于混合聚类与融合用户兴趣的协同过滤推荐算法

推荐效率低、推荐质量有待提高等问题普遍存在于传统协同过滤推荐算法中,为了改善并解决这些问题,在协同过滤推荐算法中将混合聚类与用户兴趣偏好融合,经过验证推荐质量有显著提升.首先根据用户的个人相关信息构建Canopy+bi-Kmeans的一种多重混合聚类模型,采用提出的混合聚类模型把所有用户划分成多个聚类簇,将每个用户的兴...     

面向用户群体的Web服务推荐

针对Web服务推荐中服务用户调用Web服务的服务质量数据稀疏性导致的低推荐质量问题,提出了一种面向用户群体并基于协同过滤的Web服务推荐算法(WRUG)。首先,为每个服务用户根据用户相似性矩阵构建其个性化的相似用户群体;其次,以相似用户群体中心点代替群体从而计算用户群体相似性矩阵;最后,构造面向群体的Web服务推荐公式并为目标用户预测缺失的Web服务质量。通过对197万条真实Web服务质量调用记录的数据集进行对比实验,与传统基于协同过滤的推荐算法(TCF)和基于用户群体影响的协同过滤推荐算法(CFBUGI)相比,WRUG的平均绝对误差下降幅度分别为28.9%和4.57%;并且WRUG的覆盖率上升幅度分别为110%和22.5%。实验结果表明,在相同实验条件下WRUG不仅能提高Web服务推荐系统的预测准确性,而且能显著地提高其有效预测服务质量的百分比。     

Discovering user profiles for Web personalized recommendation

With the growing popularity of the World Wide Web, large volume of user access data has been gathered automatically by Web servers and stored in Web logs. Discovering and understanding user behavior patterns from log files can provide Web personalized recommendation services. In this paper, a novel clustering method is presented for log files called Clustering large Weblog based on Key Path Model (CWKPM), which is based on user browsing key path model, to get user behavior profiles. Compared with the previous Boolean model, key path model considers the major features of users‘ accessing to the Web: ordinal, contiguous and duplicate. Moreover, for clustering, it has fewer dimensions. The analysis and experiments show that CWKPM is an efficient and effective approach for clustering large and high-dimension Web logs.     

用户个性化推荐系统的设计与实现

为实现个性化服务,理解用户兴趣就成了提供服务的关键任务,因此,提出了隐性采集用户浏览内容、用户浏览时间和用户操作时间的信息方法,通过对网络爬虫程序抓取的网页进行内容清洗提取出主要内容之后,利用VSM建立文档模型,并采用SVM分类方法建立推荐库.基于从客户端采集的用户兴趣信息建模,以及根据该模型和推荐库的相似度,给用户推荐信息.此外,给出了基于该模型的推荐原型系统的实现,使用查准率来评价该系统.试验结果表明,系统较好地实现了基于用户兴趣来推荐阅读的信息.     

Semantic user profiling techniques for personalised multimedia recommendation

Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme.     

面向不一致用户评价准则的在线服务推荐

客观上,用户的评价准则是由主观意识决定的,用户之间的评价准则不同导致多个用户对同一服务的评分不具备可比较性,不考虑不同用户评分的不可比较性所获得的服务推荐将难以满足用户个性偏好及其真实需求。为此,提出一种面向不一致用户评价准则的在线服务推荐方法,考虑用户偏好不一致时用户对在线服务的偏好关系,以偏好关系计算用户之间的相似度,并以此获得在线服务推荐结果。首先以用户-服务评分矩阵为基础建立用户对服务的偏好关系,其次根据偏好关系计算用户之间的相似度,然后以用户相似度为基础对用户未评分的服务进行评分预测,最后以预测评分的排序结果作为推荐结果。与经典的协同过滤推荐方法的比较实验,验证了本方法的有效性。实验表明,本方法获得的推荐结果能满足大多数用户的服务偏好,同时获得了比经典的协同过滤推荐方法更好的准确率。     

Preference-based user rating correction process for interactive recommendation systems

In most of the recommendation systems, user rating is an important user activity that reflects their opinions. Once the users return their ratings about items the systems have suggested, the user ratings can be used to adjust the recommendation process.However, while rating the items users can make some mistakes (e.g., natural noises). As the recommendation systems receive more incorrect ratings, the performance of such systems may decrease. In this paper, we focus on an interactive recommendation system which can help users to correct their own ratings. Thereby, we propose a method to determine whether the ratings from users are consistent to their own preferences (represented as a set of dominant attribute values) or not and eventually to correct these ratings to improve recommendation. The proposed interactive recommendation system has been particularly applied to two user rating datasets (e.g., MovieLens and Netflix) and it has shown better recommendation performance (i.e., lower error ratings).     

Graph bandit for diverse user coverage in online recommendation

We study a recommendation system problem, in which the system must be able to cover as many users’ preferences as possible while these preferences change over time. This problem can be formulated as a variation of the maximum coverage problem; specifically we introduced a novel problem of Online k-Hitting Set, where the number of sets and elements within the sets can change dynamically. When the number of distinctive elements is large, an exhaustive search for even a fixed number of elements is known to be computationally expensive. Even the static problem is known to be NP-hard (Hochba, ACM SIGACT News 28(2):40–52, 1997) and many known algorithms tend to have exponential growth in complexity. We propose a novel graph based UCB1 algorithm that effectively minimizes the number of elements to consider, thereby reducing the search space greatly. The algorithm utilizes a new rewarding scheme to choose items that satisfy more users by balancing coverage and diversity as it construct a relational graph between items to recommend. Experiments show that the new graph based algorithm performs better than existing techniques such as Ranked Bandit (Radlinski et al. 2008) and Independent Bandits (Kohli et al. 2013) in terms of satisfying diverse types of users while minimizing computational complexity.     

基于用户关注度的个性化新闻推荐系统

为满足用户需求,以用户为中心,解决用户关注度不断变化、数据稀疏性、优化时间和空间效率等问题,提出基于用户关注度的个性化新闻推荐系统。推荐系统引入个人兴趣和场景兴趣来描述用户关注度,使用雅克比度量用户相似性,对相似度加权求和预测用户关注度,从而提供给用户经过排序的新闻推荐列表。实验结果表明,推荐系统有效地提高了推荐精准度和覆盖度,改善了系统可扩展性和自动更新能力,具有良好的推荐效果。     

Group recommendation with noisy subjective preferences

Social choice theory provides a principled framework for the aggregation of individuals' preferences in support of group decision‐making and recommendation. Much of this work, however, either assumes that individuals' subjective preferences (and thus, their votes) are correctly specified by the individuals themselves, or alternatively that the votes of individuals are noisy estimates of some underlying ground truth over rankings of alternatives. We argue that neither model appropriately addresses some of the issues which arise in the context of group‐recommendation domains where individuals have subjective preferences but for some reason (eg, the high cognitive burden, concerns about privacy, etc.) may instead vote using a noisy estimate of their subjective preference rankings. In this paper, we propose a general probabilistic framework for modeling noisy subjective preferences, and explore the accuracy and reliability of four well‐studied voting rules under various noise models. Our results demonstrate that there is no single reliable method amongst the examined methods. Specifically, we observe the change in noise distribution can flip one method from being the most reliable to the least.     

用户兴趣变化和类别关联度的混合推荐算法

协同过滤算法是目前推荐系统中最普遍的个性化推荐技术。针对传统算法相似性度量方法不足的问题,提出了融合用户兴趣变化和类别关联度的混合推荐算法。算法根据用户的评分项目信息来对项目进行类别划分,挖掘出用户对不同类别项目的喜爱关注程度;同时将基于时间的兴趣度权重函数引入项目相似度计算之中来进一步提高计算的精确度,最后将改进后的相似度计算方法融入到用户聚类方法中,用户聚类之后,其所在的类别将对用户推荐准确度产生极大的作用。实验结果表明,在Movielens-1k数据集上运行该算法,该算法在运行效率和精确度上都有所提高。     

基于动态和静态偏好的兴趣点推荐算法

针对大多数现有主流兴趣点(POI)推荐算法忽略了融合用户复杂动态偏好和一般静态偏好建模的复杂性问题,提出一个融合复杂动态用户偏好和一般静态用户偏好的POI推荐算法CLSR.首先,在复杂动态偏好建模过程中,基于用户的签到行为及其中的跳过行为设计一个混合神经网络,实现用户的复杂动态兴趣的建模;其次,在一般静态偏好建模过程中...     

Modelling and learning user preferences over sets

Although there has been significant research on modelling and learning user preferences for various types of objects, there has been relatively little work on the problem of representing and learning preferences over sets of objects. We introduce a representation language, DD-PREF, that balances preferences for particular objects with preferences about the properties of the set. Specifically, we focus on the depth of objects (i.e. preferences for specific attribute values over others) and on the diversity of sets (i.e. preferences for broad vs. narrow distributions of attribute values). The DD-PREF framework is general and can incorporate additional object- and set-based preferences. We describe a greedy algorithm, DD-Select, for selecting satisfying sets from a collection of new objects, given a preference in this language. We show how preferences represented in DD-PREF can be learned from training data. Experimental results are given for three domains: a blocks world domain with several different task-based preferences, a real-world music playlist collection, and rover image data gathered in desert training exercises.     

考虑非对称用户偏好的推荐算法

为解决常见的相似性方法存在未考虑用户间共同评分项在目标用户所评项目中的比例以及用户评分偏好的问题。提出了非对称因子和偏好因子,用于提高用户相似性计算的准确性。在公开的MovieLens和Yahoo Music数据集上的实验表明,引入这两个因子后,相似性模型的预测误差下降显著,优于其他相似性方法。非对称因子和偏好因子的引入更合理地体现出用户间的评分差异性,有效地处理了用户偏好问题,提高了推荐质量。