Personalized Standard Deviations Improve the Baseline Estimation of Collaborative Filtering Recommendation

Baseline estimation is a critical component for latent factor-based collaborative filtering (CF) recommendations to obtain baseline predictions by evaluating global deviations for both users and items from personalized ratings. Classical baseline estimation presupposes that the user’s factual rating range is the same as the system’s given rating range. However, from observations on real datasets of movie recommender systems, we found that different users have different actual rating ranges, and users can be classified into four kinds according to their personalized rating criterion, including normal, strict, lenient, and middle. We analyzed ratings’ distributions and found that the proportion of user ratings’ local standard deviation to the system’s global standard deviation is equal to that of the user’s actual rating range to the system’s rating range. We propose an improved and unified baseline estimation model based on the standard deviation’s proportion to alleviate the influence of classical baseline estimation’s limitation. We also apply the proposed baseline estimation model in existing latent factor-based CF recommendations and propose two instances. We performed experiments on full ratings of datasets by cross evaluations, including Flixster, Movielens (10 M), Movielens (latest small), FilmTrust, and MiniFilm. The results prove that the proposed baseline estimation model has better predictive accuracy than the classical model and is efficient in improving prediction performance for existing latent factor-based CF recommendations.

A Systematized FMEA Practice for Magnet Relays

The Design Failure Modes and Effects Analysis (DFMEA) are generally applied to risk management of New Product Development (NPD) through standardization of potential failure modes and effect-ranking of rating criterion with failure modes. Typical 1 to 10 of effect-ranking are widely weighed the priority of classification, that framing effects and status quo senses might cause decision trap happening thus. The FMEA follows considerable indexes which are including Severity, Occurrence and Detection, and need be associated with difference between every two failures individually. However, we suspect that a more systematic construction of the analysis by which failure modes belong is necessary in order to make intellectual progress in this area. Two ways of such differentiation and construction are improvable effect-ranking and systematized indexes; here we resolve for attributes of failures with classification, maturity and experiance of indexes according to an existing rule. In Severity model, the larger differentiation is achieved by separating indexes to the classification of the Law & Regulation, Function and Cosmetic. Occurrence model has its characteristic a reliable ranking indexwhich assists decisionmakers to manage their venture. This is the model most closely associate with product maturity by grouping indexes to the new, extend and series product. Detection model offers a special perspective on cost; here the connections concerned with phase occasion of the review, verification and validation. Such differentiations will be proposed and mapped with the Life Cycle Profile (LCP) to systematize FMEA. Meanwhile, a more reasonable Risk Priority Number (RPN) with the new weighting rule will be worked out for effect-ranking and management system will be integrated systematiclly

FUZZY OPERATOR TREES FOR MODELING RATING FUNCTIONS

We introduce a new method for modeling rating (utility) functions which employs techniques from fuzzy set theory. The main idea is to build a hierarchical model, called a fuzzy operator tree (FOT), by recursively decomposing a rating criterion into sub-criteria, and to combine the evaluations of these sub-criteria by means of suitable aggregation operators. Apart from the model conception itself, we propose an evolutionary method for model calibration that fits the parameters of an FOT to exemplary ratings. The possibility to adapt an FOT to a given set of data makes the approach also interesting from a machine learning point of view. The performance of the approach is evaluated by means of a suitable experimental study.

A Rating Criterion for Film Resistors

After experimental work has been carried out to determine the required constants, the resistor film temperature may be calculated from its dimensions and power dissipation using an expression which takes account of, and makes a reasonably good approximation for, the heat dissipated by lateral conduction. The arithmetic is simple and well suited to rapid processing using a programmed electronic calculator.

Leadership Aspiration and Leadership Ability

The study explored the question whether a significant relationship exists between the individual's aspiring to be in a leadership position and his leadership ability as estimated by peer ratings. Leadership aspiration was measured once by the Survey of Interpersonal Values (SIV); leadership ability was measured twice, once in each of two different group settings. Ss were men in two companies ( N = 246 and N = 229) of Army basic trainees. For each company, in each of two group settings, the Leadership scale was found to be significantly related to peer ratings of leadership ability. In all four instances, significant negative relationships were found between the Support scale of the SIV and the peer-rating criterion. These findings indicate that certain measurable aspects of the individual's need or value structure may have potential for predicting certain leadership criteria.