Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a recommendation system must choose between a set of candidate approaches. A first step towards selecting an appropriate algorithm is to decide which properties of the application to focus upon when making this choice. Indeed, recommendation systems have a variety of properties that may affect user experience, such as accuracy, robustness, scalability, and so forth. In this paper we discuss how to compare recommenders based on a set of properties that are relevant for the application. We focus on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. We describe experimental settings appropriate for making choices between algorithms. We review three types of experiments, starting with an offline setting, where recommendation approaches are compared without user interaction, then reviewing user studies, where a small group of subjects experiment with the system and report on the experience, and finally describe large scale online experiments, where real user populations interact with the system. In each of these cases we describe types of questions that can be answered, and suggest protocols for experimentation. We also discuss how to draw trustworthy conclusions from the conducted experiments. We then review a large set of properties, and explain how to evaluate systems given relevant properties. We also survey a large set of evaluation metrics in the context of the properties that they evaluate.

/pdf/evaluating-recommendation-systems-48fl85cbst.pdf

Evaluating Recommendation Systems

在翟象俊主编的《大学英语》第2册第5单元中，有这样一个句子：“As a boy and then as an adult, I never lost my wonder at the personality that was Einstein.”教参中指出“the personality that was Einstein”应理解为“the personality which was the most striking characteristic of Einstein”，该句译为“作为一个孩子，到后来作为一个成人，我一直对爱因斯坦的个性惊叹不已”。很明显，在这里译者把“personality”理解为“个性，人格”，但本人认为应译为“人物，名人”更妥。“personality”可作“个性，人格”讲，但它还有另外一个重要意思。在陆谷孙主编的《英汉大词典》（1993年版）中，“personality”第3条释义为：“个人，人物，名人”：“appoint a personality to lead a campaign，派一个人去领导一场运动”。“a personality in the news，新闻人物”。在...

此处“personality”译法探析

https://cyberleninka.org/article/n/501521.pdf

Recommendation systems: Principles, methods and evaluation

Speeded Up Robust Features

This research was motivated by our interest in understanding the criteria for measuring the success of a recommender system from users' point view. Even though existing work has suggested a wide range of criteria, the consistency and validity of the combined criteria have not been tested. In this paper, we describe a unifying evaluation framework, called ResQue (Recommender systems' Quality of user experience), which aimed at measuring the qualities of the recommended items, the system's usability, usefulness, interface and interaction qualities, users' satisfaction with the systems, and the influence of these qualities on users' behavioral intentions, including their intention to purchase the products recommended to them and return to the system. We also show the results of applying psychometric methods to validate the combined criteria using data collected from a large user survey. The outcomes of the validation are able to 1) support the consistency, validity and reliability of the selected criteria; and 2) explain the quality of user experience and the key determinants motivating users to adopt the recommender technology. The final model consists of thirty two questions and fifteen constructs, defining the essential qualities of an effective and satisfying recommender system, as well as providing practitioners and scholars with a cost-effective way to evaluate the success of a recommender system and identify important areas in which to invest development resources.

/pdf/a-user-centric-evaluation-framework-for-recommender-systems-54or09bmm1.pdf

A user-centric evaluation framework for recommender systems

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users' perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS's ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users' adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system's recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system's overall perceptive qualities and how these qualities influence users' behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing methods.

/pdf/evaluating-recommender-systems-from-the-user-s-perspective-3ktpp8tatu.pdf

Evaluating recommender systems from the user's perspective: survey of the state of the art

Collaborative filtering (CF), one of the most successful recommendation approaches, continues to attract interest in both academia and industry. However, one key issue limiting the success of collaborative filtering in certain application domains is the cold-start problem, a situation where historical data is too sparse (known as the sparsity problem), new users have not rated enough items (known as the new user problem), or both. In this paper, we aim at addressing the cold-start problem by incorporating human personality into the collaborative filtering framework. We propose three approaches: the first is a recommendation method based on users' personality information alone; the second is based on a linear combination of both personality and rating information; and the third uses a cascade mechanism to leverage both resources. To evaluate their effectiveness, we have conducted an experimental study comparing the proposed approaches with the traditional rating-based CF in two cold-start scenarios: sparse data sets and new users. Our results show that the proposed CF variations, which consider personality characteristics, can significantly improve the performance of the traditional rating-based CF in terms of the evaluation metrics MAE and ROC sensitivity.

/pdf/enhancing-collaborative-filtering-systems-with-personality-39wz5w1igm.pdf

Enhancing collaborative filtering systems with personality information

Video Stabilization is one of those important video processing techniques to remove the unwanted camera vibration in a video sequence. In this paper, we present a practical method to remove the annoying shaky motion and reconstruct a stabilized video sequence with good visual quality. Here, the scale invariant (SIFT) features, proved to be invariant to image scale and rotation, is applied to estimate the camera motion. The unwanted vibrations are separated from the intentional camera motion with the combination of Gaussian kernel filtering and parabolic fitting. It is demonstrated that our method effectively removes the high frequency 'noise' motion, but also minimize the missing area as much as possible. To reconstruct the undefined areas, resulting from motion compensation, we adopt the mosaicing method with Dynamic Programming. The proposed method has been confirmed to be effective over a widely variety of videos.

Video Stabilization Using Scale-Invariant Features

Our previous research indicates that using personality quizzes is a viable and promising way to build user profiles to recommend entertainment products Based on these findings, our current research further investigates the feasibility of using personality quizzes to build user profiles not only for an active user but also his or her friends We first propose a general method that infers users' music preferences in terms of their personalities Our in-depth user studies show that while active users perceive the recommended items to be more accurate for their friends, they enjoy more using personality quiz based recommenders for finding items for themselves Additionally, we explore if domain knowledge has an influence on users' perception of the system We found that novice users, who are less knowledgeable about music, generally appreciated more personality-based recommenders Finally, we propose some design issues for recommender systems using personality quizzes.

/pdf/a-study-on-user-perception-of-personality-based-recommender-49vv07lr13.pdf

Rong Hu

Papers

A user-centric evaluation framework for recommender systems

Evaluating recommender systems from the user's perspective: survey of the state of the art

Enhancing collaborative filtering systems with personality information

Video Stabilization Using Scale-Invariant Features

A study on user perception of personality-based recommender systems