考虑用户活跃度和项目流行度的基于项目最近邻的协同过滤算法

项目相关性度量是基于项目最近邻的协同过滤算法的关键。已有的项目相关性度量方法在数据集稀疏或推荐低流行度产品时会面临较大挑战,因此提出一种考虑用户活跃度和项目流行度的基于项目最近邻的协同过滤算法。该算法在度量两个项目的相关性时,若有记录只对两个项目之一有评分,则利用该记录所对应的评分用户的活跃度和被评价项目的流行度进行相关性惩罚,从而提高数据稀疏环境下低流行度产品被推荐的概率。实验表明,所提算法在保证评分预测精度的情况下提升了推荐结果的多样性和新颖性。     

Userrank for item-based collaborative filtering recommendation

With the recent explosive growth of the Web, recommendation systems have been widely accepted by users. Item-based Collaborative Filtering (CF) is one of the most popular approaches for determining recommendations. A common problem of current item-based CF approaches is that all users have the same weight when computing the item relationships. To improve the quality of recommendations, we incorporate the weight of a user, userrank, into the computation of item similarities and differentials. In this paper, a data model for userrank calculations, a PageRank-based user ranking approach, and a userrank-based item similarities/differentials computing approach are proposed. Finally, the userrank-based approaches improve the recommendation results of the typical Adjusted Cosine and Slope One item-based CF approaches.     

基于个性化情境和项目类别的资源推荐研究

传统的协同过滤推荐技术没有考虑影响用户评分的用户情境信息,但最近研究发现用户个性化情境信息直接影响着用户评分,因此在传统的协同过滤技术基础上引入用户个性化情境后推荐效果有所提高。此外可以将用户个性化情境和项目类别相结合起来。先对项目进行分类,然后再确定用户在每个项目类别下的个性化情境,同一项目类别下所有项目的用户个性化情境是相同的。在为目标项目预测评分时,先确定目标项目所在的类别,进而确定计算目标项目预测评分所用到的用户个性化情境。实验结果表明,改进后的算法较Slope one有较大提高。     

Improving neighborhood based Collaborative Filtering via integrated folksonomy information

Personalized recommender systems which can provide people with suggestions according to individual interests usually rely on Collaborative Filtering (CF). The neighborhood based model (NBM) is a common choice when implementing such recommenders due to the intuitive nature; however, the recommendation accuracy is a major concern. Current NBM based recommenders mostly address the accuracy issue based on the rating data alone, whereas research on hybrid recommender systems suggests that users enjoy specifying feedback about items across multiple dimensions. In this work we aim to improve the accuracy of NBM via integrating the folksonomy information. To achieve this objective, we first propose the folksonomy network (FN) to analyze the item relevance described by the folksonomy data. We subsequently integrate the obtained folksonomy information into the global-optimization based NBM for making multi-source based recommendations. Experiments on the MovieLens dataset suggest positive results, which prove the efficiency of our strategy.     

On extracting recommendation knowledge for personalized web-based learning based on ant colony optimization with segmented-goal and meta-control strategies

Personalized web-based learning has become an important learning form in the 21st century. To recommend appropriate online materials for a certain learner, several characteristics of the learner, such as his/her learning style, learning modality, cognitive style and competency, need to be considered. An earlier research result showed that a fuzzy knowledge extraction model can be established to extract personalized recommendation knowledge by discovering effective learning paths from past learning experiences through an ant colony optimization model. Though that results revealed the theoretical potential of the proposed method in discovering effective learning paths for learners, critical limitations arose when considering its applications in real world situations, such as the requirement of a large amount of learners and a long period of training cycles in order to discover good learning paths for learners. These practical issues motivate this research. In this paper, the aim is to resolve the aforementioned issues by devising more efficient algorithms that basically run on the same ant colony model yet requiring only a reasonable number of learners and training cycles to find satisfactory good results. The key approaches to resolving the practical issues include revising the global update policy, an adaptive search policy and a segmented-goal training strategy. Based on simulation results, it is shown that these new ingredients added to the original knowledge extraction algorithm result in more efficient ones that can be applied in practical situations.     

一种个性化旅游线路推荐算法

采用用户-景点-线路三部图来描述用户的行为,通过改进的随机行走算法给用户推荐合适的旅游线路,可以提供准确的旅游线路推荐并有效地解决新的线路难以推荐的问题。通过对景点的聚类,减小了数据稀疏性对推荐带来的影响并避免了过拟合问题。实验结果表明,与传统的方法相比,本文提出的算法具有较好的排序准确度,特别是对稀疏度较高的用户,优势更明显。     

决策树分类算法在个性化图书推荐中的应用

采用数据挖掘中的决策树方法,对图书馆的图书借阅数据进行研究和分析,提出了基于读者阅读兴趣的个性化图书推荐模型。实验表明,该模型能够为新老读者有针对性地推荐新书,较好地实现了图书馆的个性化的创新服务。     

Espresso Reward Learning, Hold the Dopamine: Theoretical Comment on Robinson et al. (2005).

Question: Is dopamine needed for reward learning? Answer: No--at least, not in the brain of a caffeinated dopamine-deficient (DD) mutant mouse. That is the conclusion of an important paper in this issue by S. Robinson, S. M. Sandstrom, V. H. Denenberg, and R. D. Palmiter (see record 2005-01705-001). Those authors demonstrate that reward learning can proceed normally in the brains of DD mice, even though they contain no dopamine at the time of learning, if the mice are given caffeine just before learning. Caffeine activates the DD mice by a nondopaminergic mechanism, allowing them to learn where to obtain food reward in a T-maze runway. Their reward-learning-without-dopamine is revealed on a subsequent test day, when dopamine function is restored by L-dopa administration. Robinson et al. conclude that dopamine is not needed for normal learning about rewards, or for hedonic "liking" of rewards during learning, but rather specifically for a motivational "wanting" component of reward, such as incentive salience. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Genetic Correlational Analyses of Ethanol Reward and Aversion Phenotypes in Short-Term Selected Mouse Lines Bred for Ethanol Drinking or Ethanol-Induced Conditioned Taste Aversion.

Short-term selective breeding created mouse lines divergent for ethanol drinking (high drinking short-term selected line [STDRHI], low drinking [STDRLO]) or ethanol-induced conditioned taste aversion (CTA; high [HTA], low [LTA]). Compared with STDRLO, STDRHI mice consumed more saccharin and less quinine, exhibited greater ethanol-induced conditioned place preference (CPP), and showed reduced ethanol stimulation and sensitization under some conditions; a line difference in ethanol-induced CTA was not consistently found. Compared with LTA, HTA mice consumed less ethanol but were similar in saccharin consumption, sensitivity to ethanol-induced CPP, and ethanol-induced locomotor stimulation and sensitization. These data suggest that ethanol drinking is genetically associated with several reward-and aversion-related traits. The interpretation of ethanol-induced CTA as more genetically distinct must be tempered by the inability to test the CTA lines beyond Selection Generation 2. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reactions to Counterstereotypic Behavior: The Role of Backlash in Cultural Stereotype Maintenance.

Social and economic sanctions for counterstereotypical behavior have been termed the backlash effect (L. A. Rudman, 1998). The authors present a model of the role of backlash in cultural stereotype maintenance from the standpoint of both perceivers and actors. In Experiments 1 and 2, participants lost a competition to either atypical or typical men or women and subsequently showed greater tendency to sabotage deviants. Moreover, undermining deviants was associated with increased self-esteem, suggesting that backlash rewards perceivers psychologically. Experiment 3 showed that gender deviants who feared backlash resorted to strategies designed to avoid it (e.g., hiding, deception, and gender conformity). Further, perceivers who sabotaged deviants (Experiment 2) or deviants who hid their atypicality (Experiment 3) estimated greater stereotyping on the part of future perceivers, in support of the model's presumed role for backlash in stereotype maintenance. The implications of the findings for cultural stereotypes are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)