Research and Applications of the Processes of Performance Appraisal: A Bibliography of Recent Literature, 1981-1989

[Excerpt] There have been several recent reviews of different subtopics within the general performance appraisal literature. The reader of these reviews will find, however, that the accompanying citations may be of limited utility for one or more reasons. For example, the reference sections of these reviews are usually composed of citations which support a specific theory or practical approach to the evaluation of human performance. Consequently, the citation lists for these reviews are, as they must be, highly selective and do not include works that may have only a peripheral relationship to a given reviewer\u27s target concerns. Another problem is that the citations are out of date. That is, review articles frequently contain many citations that are fifteen or more years old. The generation of new studies and knowledge in this field occurs very rapidly. This creates a need for additional reference information solely devoted to identifying the wealth of new research, ideas, and writing that is changing the field

The contribution of injury severity, executive and implicit functions to awareness of defi cits after traumatic brain injury (TBI)

Deficits in self-awareness are commonly seen after Traumatic Brain Injury (TBI) and adversely affect rehabilitative efforts, independence and quality of life (Ponsford, 2004). Awareness models predict that executive and implicit functions are important cognitive components of awareness though the putative relationship between implicit and awareness processes has not been subject to empirical investigation (Crosson et al., 1989; Ownsworth, Clare, & Morris, 2006; Toglia & Kirk, 2000). Severity of injury, also thought to be a crucial determinant of awareness outcome post-insult, is under-explored in awareness studies (Sherer, Boake, Levin, Silver, Ringholz, & Walter, 1998 ). The present study measured the contribution of injury severity, IQ, mood state, executive and implicit functions to awareness in head-injured patients assigned to moderate/severe head-injured groups using several awareness, executive, and implicit measures. Severe injuries resulted in greater impairments across most awareness, executive and implicit measures compared with moderate injuries, although deficits were still seen in the moderate group. Hierarchical regression results showed that severity of injury, IQ, mood state, executive and implicit functions made signifi cant unique contributions to selective aspects of awareness. Future models of awareness should account for both implicit and executive contributions to awareness and the possibility that both are vulnerable to disruption after neuropathology. ( JINS , 2010, 16 , 1– 10 .

The HR-Firm Performance Relationship: Can it be in the Mind of the Beholder?

This study examined whether respondents’ implicit theories of performance could impact their responses to surveys regarding HR practices and effectiveness. Senior Human Resource and Line Executives and MBA, graduate Engineering, and graduate HR students read scenarios of high and low performing firms and were asked to report on the prevalence of various HR practices and effectiveness of the HR function in each firm. Results indicated that all four groups of respondents held implicit theories that high performing firms were characterized by extensive HR practices and had highly effective HR functions relative to low performing firms. Subjects with substantial work experience reported greater differences between and high and low performing firms than did subjects with relatively little work experience. The implications of these results for research on the HR Practices – Firm Performance relationship are discussed

BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation

Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation

Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure