Basics of qualitative research: Grounded theory procedures and techniques

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Trust: The Social Virtues and the Creation of Prosperity

The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.

The MovieLens Datasets: History and Context

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.

/pdf/trust-aware-recommender-systems-1umffna3j1.pdf

Trust-aware recommender systems

Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation can increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improvements are achieved for users who provided few ratings.

Trust-Aware Collaborative Filtering for Recommender Systems

In today's connected world it is possible and very common to interact with unknown people, whose reliability is unknown. Trust Metrics are a recently proposed technique for answering questions such as "Should I trust this user?". However, most of the current research assumes that every user has a global quality score and that the goal of the technique is just to predict this correct value. We show, on data from a real and large user community, Epinions.com, that such an assumption is not realistic because there is a significant portion of what we call controversial users, users who are trusted and distrusted by many. A global agreement about the trustworthiness value of these users cannot exist. We argue, using computational experiments, that the existence of controversial users (a normal phenomena in societies) demands Local Trust Metrics, techniques able to predict the trustworthiness of an user in a personalized way, depending on the very personal view of the judging user.

/pdf/controversial-users-demand-local-trust-metrics-an-5ge4awdrrn.pdf

Controversial users demand local trust metrics: an experimental study on Epinions.com community

Recommender systems (RS) have been used for suggesting items (movies, books, songs, etc.) that users might like. RSs compute a user similarity between users and use it as a weight for the users’ ratings. However they have many weaknesses, such as sparseness, cold start and vulnerability to attacks. We assert that these weaknesses can be alleviated using a Trust-aware system that takes into account the “web of trust” provided by every user.

Using Trust in Recommender Systems: An Experimental Analysis

Recommender Systems (RS) suggests to users items they will like based on their past opinions. Collaborative Filtering (CF) is the most used technique to assess user similarity between users but very often the sparseness of user profiles prevents the computation. Moreover CF doesn't take into account the reliability of the other users. In this paper we present a real world application, namely moleskiing.it, in which both of these conditions are critic to deliver personalized recommendations. A blog oriented architecture collects user experiences on ski mountaineering and their opinions on other users. Exploitation of Trust Metrics allows to present only relevant and reliable information according to the user's personal point of view of other authors trustworthiness. Differently from the notion of authority, we claim that trustworthiness is a user centered notion that requires the computation of personalized metrics. We also present an open information exchange architecture that makes use of Semantic Web formats to guarantee interoperability between ski mountaineering communities.

Paolo Massa

Papers

Trust-aware recommender systems

Trust-Aware Collaborative Filtering for Recommender Systems

Controversial users demand local trust metrics: an experimental study on Epinions.com community

Using Trust in Recommender Systems: An Experimental Analysis

A trust-enhanced recommender system application: Moleskiing