Francesco Epifania

User profiling vs. accuracy in recommender system user experience

A Recommender System (RS) filters a large amount of information to identify the items that are likely to be more interesting and attractive to a user. Recommendations are inferred on the basis of different user profile characteristics, in most cases including explicit ratings on a sample of suggested elements. RS research highlights that profile length, i. e., the number of collected ratings, is positively correlated to the accuracy of recommendations, which is considered an important quality factor for RSs. Still, gathering ratings adds a burden on the user, which may negatively affect the UX. A design tension seems to exist, induced by two conflicting requirements -- to raise accuracy by increasing the profile length, and to make the profiling process smooth for the user by limiting the number of ratings. The paper presents a wide empirical study (1080 users involved) which explores this issue. Our work attempts to identify which of the two contrasting forces influenced by profile length -- recommendations accuracy and burden of the rating process - has stronger effects on the perceived quality of the UX with a RS.

Towards a social e-learning platform for demanding users

In this paper we introduce a platform tailored to give teachers and trainers appropriate knowledge, skills, and innovative tools in the domain of the entrepreneurial education. To this end, a social network is created where teachers can formally or informally share experiences supporting their peers with technical training, along with theory and practical examples deriving from mutual and practical experiences in entrepreneurship. In this perspective, the platform through a systematic and intelligent use of metadata is able to offer an innovative social network specially tailored for teachers in order to valorize their competencies and to fit their expectations.

User-Centered Evaluation of Recommender Systems with Comparison between Short and Long Profile

The growth of the social web poses new challenges and opportunities for recommender systems. The goal of Recommender Systems (RSs) is to filter information from a large data set and to recommend to users only the items that are most likely to interest and/or appeal to them. The quality of a RS is typically defined in terms of different attributes, the principal ones being relevance, novelty, serendipity and global satisfaction. Most existing works evaluate quality of Recommender Systems in terms of statistical factors that are algorithmically measured. This paper aims to explore whether (i) algorithmic measures of RS quality are in accordance with user-based measure and (ii) the user perceived quality of a RS is affected by the number of movies rated by the user. For this purpose we designed, developed and tested a web recommender system, TheBestMovie4You (http://www.moviers.it), which allows us to collect questionnaires about the quality of recommendations. We made a questionnaire and gave it to 240 subjects and we wanted to have as wide a set of users as possible using social web. In a experiment we asked the users to choose five movies (short profile), in a second to choose ten (long profile). Our results show that statistical properties fail in fully describing the quality of algorithms, because with user-centered metrics we can outline an algorithms features that otherwise could not be detected. The comparison between the two phases highlighted a difference only in three cases out of twenty.

Evaluation of Requirements Collection Strategies for a Constraint-based Recommender System in a Social e-Learning Platform

The NETT Recommender System (NETT-RS) is a constraint-based recommender system that recommends learning resources to teachers who want to design courses. As for many state-of-the-art constraint-based recommender systems, the NETT-RS bases its recommendation process on the collection of requirements to which items must adhere in order to be recommended. In this paper we study the effects of two different requirement collection strategies on the perceived overall recommendation quality of the NETT-RS. In the first strategy users are not allowed to refine and change the requirements once chosen, while in the second strategy the system allows the users to modify the requirements (we refer to this strategy as backtracking). We run the study following the well established ResQue methodology for user-centric evaluation of RS. Our experimental results indicate that backtracking has a strong positive impact on the perceived recommendation quality of the NETT-RS.

A Rule Based Recommender System

We introduce a new recommending paradigm based on the genomic features of the candidate objects. The system is based on the tree structure of the object metadata which we convert in acceptance rules, leaving the user the discretion of selecting the most convincing rules for her/his scope. We framed the deriving recommendation system on a content management platform within the scope of the European Project NETT and tested it on the Entree UCI benchmark.

e-Teaching Assistant

With the ambition of providing teachers with a concrete tool for worldwide exploiting didactic contents to feature their courses, we face the problem of creating a social platform with adequate functionalities to satisfy the teacher expectations. Starting with a well designed architecture we endow it with three key functionalities that become the stakeholders of the emerging social network: 1) a quality system ensuring the value of the materials the users put in the platform repository as their contribution to the social business, 2) a recommender system based on computational intelligence techniques constituting the principal tool to guide teachers along the assembling of materials into courses, and 3) a gamification system, root of the no-profit business plan of the platform, to involve teachers in the social network. As a result we delineate an ecosystem where teachers exploit contents of a repository to which contribute by themselves. They are encouraged in exploiting and contributing because the contents are of high quality; they are wisely assisted in the exploration of the repository by platform services yet under their full control; and they are variously reworded by this involvement.

Social metaservices for the collaborative production of learning modules

In queste pagine si introduce una piattaforma a servizio degli usuali Learning Management Systems (LMS) per consentirne un utilizzo facile e proficuo da parte dei docenti di un determinato settore. Nello specifico la piattaforma costituisce il supporto informativo a un ampio progetto di promozione dell’educazione all’imprenditorialita lanciato dalla Comunita Europea. La chiave di volta e costituta dai metadati con cui sono descritti i suoi contenuti. Questi metadati sono alla base delle procedure d’interrogazione e raccomandazione, nonche di altre azioni “social” sulle quali i docenti possono contare per reperire il materiale su cui fondare i corsi che intendono erogare. In tal prospettiva la piattaforma s’identifica con una social network per utenti esigenti, appunto i docenti, che si aspettano di reperire nel sistema materiali autorevoli e appropriati, essendo capaci di valutarne tali aspetti e al contempo desiderosi di venire guidati nella loro ricerca all’interno dell’ampio repertorio messo a disposizione dalla piattaforma.