The motivational bases of attitude change.

In describing the motivational forces which determine attitude formation and change, the authors focus upon three basic variables: reward and punishment, reality testing, and ego defensiveness. Methodological approaches which seem to lead to appropriate tests of hypotheses generated by the discussion are presented. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Delay of gratification, motivation for the prohibited gratification, and responses to temptation.

Yielding to temptation, in a situation in which attainment of achievement rewards is contingent upon deviant (cheating) behavior, was conceptualized to be a function of (a) the strength of the motivation to attain the prohibited gratification, and (b) the inability to delay immediate gratification. 6th-grade boys participated in an experimentally controlled "shooting gallery" game of skill in which attainment of achievement rewards (prizes) was contingent upon the child's falsifying his own scores. Motivation for the prohibited gratification was inferred from "n Achievement" scores: preference for immediate, smaller (ImR) or delayed, larger (DelR) rewards in choice situations was the index of the ability to delay gratification. Achievement motivation was related to the S's producing sufficiently deviant scores to obtain an achievement reward, and preference for DelR was related negatively to the amount of cheating and positively to the latency of cheating, i.e., the number of trials before the occurrence of the 1st deviation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Apology.

Reports an error in the original article by D. Shakow (American Psychologist, 1968, 23, pp. 87-96). I am mortified to have overdone the l's in my article, "On the Rewards (and, Alas, Frustrations) of Public Service". It was not General Stillwell who was responsible for illegitimati non carborundum, but General Stilwell. Fortunately, one of the proper Stilwells of psychology was around to bring me into line. I apologize to all the "single-elled" Stilwells. (The following abstract of this article originally appeared in record 1968-09533-001.) Urges that government support also extend to creative work and individuality as government operations expand. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于大数据的高校图书馆个性化服务路径

高校图书馆具有双重属性,其一为管理属性,管理对象为馆内图书资料;其二为服务属性,服务对象为本校广大师生。在高等教育深化改革背景下,高校学生的图书使用需求更加多样化,图书馆方面也必须创新服务观念,向学生提供更加个性化的服务。大数据时代的到来,为图书馆开展个性化服务提供了技术支持,依托大数据分析和获取学生的信息需求,让图书服务更加优质化和个性化。     

Little rewards,big changes: Using exercise analytics to motivate sustainable changes in physical activity

Even using simple techniques like taking the stairs, many individuals struggle to maintain the motivation to be physically active. Health gamification systems can aid this goal by providing points earned through exercise that are redeemable for tangible extrinsic rewards. Using self-determination theory, we conduct research on one such system and investigate rewards’ effectiveness to promote exercise considering reward value, redemption frequency patterns, and fitness levels. We find that rewards do significantly increase activity levels, and this effect is larger for advanced users who redeem multiple times for higher value rewards. We close by offering future research avenues and advice to optimize reward portfolios.     

Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network

Personalized recommendation algorithms, which are effective means to solve information overload, are popular topics in current research. In this paper, a recommender system combining popularity and novelty (RSCPN) based on one-mode projection of weighted bipartite network is proposed. The edge between a user and item is weighted with the item’s rating, and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users. RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender system. We verify and compare the accuracy, diversity and novelty of the proposed model with those of other models, and results show that RSCPN is feasible.     

Pharmaceutical formulation and manufacturing using particle/powder technology for personalized medicines

Particle/powder technology is used in the manufacture of many pharmaceutical products, and research on the physical properties of particles in the nano- to micro-particle range is important in the pharmaceutical field. The concept of precision medicine will require an increasing shift in pharmaceutical manufacturing toward the design of individualized products. This perspective article focuses on particle design and powder technology for advanced formulations that will be needed in the future for individualized drug formulations and on-demand production. Nanoparticles as drug carriers in drug delivery systems will require particle designs to meet the treatment requirements of individual patients with a particular disease. In pharmaceutical manufacturing, process intensification, such as continuous manufacturing and integrated drug production from drug synthesis to final formulation, has attracted increased attention. Digital design approaches, such as artificial intelligence based on computer-aided development, also will be increasingly used. Continuous production of pharmaceutical products enables downsizing of manufacturing equipment, and on-demand manufacturing equipment has been developed. In addition, additive manufacturing, such as 3D printing, is considered to be suitable for personalized formulations, and small-scale powder handling and predictive modeling of powder characterization will be important for individual preparations.     

一种融合多因素社交活动个性化推荐模型

针对当前网络社交活动个性化推荐精度较低的问题,融合用户对活动兴趣度、召集者影响力以及地理位置偏好等三方面因素,提出一种融合多因素社交活动个性推荐模型。采用LDA文件主题模型求取用户与其参加过的所有社交活动的主题分布,利用隐含主题概率分布来表征用户的兴趣度,并构建用户与召集者间的影响力矩阵。根据活动举办地与用户常住地,建立距离幂律分布,并结合用户参加活动的频数,建立用户地理位置偏好概率模型。采用不同权值配比,综合三方面的因素形成最终的社交活动个性推荐。对比实验表明,该算法与三个因素个性推荐算法相比,准确率至少提高了36.7%,召回率至少提高了35.9%;与其他两个同类网络社交活动推荐算法相比准确率至少提高了8.77%,召回率至少提高了8.57%。     

基于马尔可夫聚类和混合协同过滤的电视节目推荐

针对家庭用户的电视节目个性化推荐问题,提出一种基于马尔可夫聚类和混合协同过滤(MCL-HCF)算法的混合推荐方法。采用马尔可夫聚类对各个时间段的电视用户进行聚类,产生不同的群组,最小化每个群组里的个体成员和群组整体的偏好差异,再以群组为单位进行电视节目推荐;使用基于物品的协同过滤和基于用户的协同过滤算法分别产生推荐列表;采用基于加权融合的混合推荐算法对两个推荐列表进行处理,得到最终的混合推荐结果。在公开数据集上的实验结果表明,该算法在平衡推荐惊喜度和相关性的同时能够获得令人满意的推荐准确率。     

基于贝叶斯后验概率和非合作博弈的推荐算法

针对传统协同过滤推荐算法推荐精度较低等问题,提出一种基于贝叶斯后验概率预测和非合作博弈的个性化推荐算法。采用文件主题模型求取用户与其参加过的所有社交活动的主题分布,利用隐含主题概率分布表征用户兴趣度,根据信任传递机制求取用户的直接信任和间接信任,形成用户间的信任度;将用户的兴趣度和信任度等隐式特征赋予合理的先验分布,利用贝叶斯后验概率预测隐式特征后的显式反馈;依据显式反馈将推荐结果转化为非合作博弈中用户效益最大化的纳什均衡求解。仿真对比实验表明,与其他三种推荐算法相比该算法的查准率至少提高了3.13%,查全率至少提高了2.62%。