Silvana Aciar

Recommender System Based on Consumer Product Reviews

Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

A multi agent recommender system that utilises consumer reviews in its recommendations

Consumer reviews, opinions and shared experiences in the use of a product form a powerful source of information about consumer preferences that can be used for making recommendations. A novel approach, which utilises this valuable information sources first time to create recommendations in recommender agents was recently developed by Aciar et al. (2007). This paper presents a general framework of this approach. The proposed approach is demonstrated using digital camera reviews as an example.

Informed Recommender Agent: Utilizing Consumer Product Reviews through Text Mining

Consumer reviews, opinions and shared experiences in the use of a product form a powerful source of information about consumer preferences that can be used for making recommendations. A novel framework, which utilizes this valuable information sources first time to create recommendations in recommender agents was recently developed by the authors. In this recommender agent, the most critical issue is how to convert the review comments into ontology instances that can be understood and utilized by computers. This problem was not addressed in our previous work. This paper presents an automatic mapping process using text mining techniques. The ontology contains a controlled vocabulary and their relationships. The attributes of the ontology are learnt from the semantic features in the review comments using supervised learning techniques. The proposed approach is demonstrated using a case study of digital camera reviews

A Negotiation-Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality but also with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems (DBEs), as well as related services like open-id, trust management, monitors, and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up ONEs that are stable, a basic condition for predictable and reliable business environments. Aiming to build stable DBEs by means of improved collective intelligence, we introduce a model of negotiation-style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor and a novel negotiation-style recommender (NSR). The ecosystem monitor provides hints to the NSR to achieve greater stability of ONE in a DBE. The greater stability provides the small companies with higher predictability and, therefore, better business results. The NSR is implemented with a simulated-annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of ONE populated by Italian companies.

Adaptive business intelligence for an open negotiation environment

Engineering of negotiation model allows to develop effective heuristic for business intelligence. Digital Ecosystems demand open negotiation models. To define in advance effective heuristics is not compliant with the requirement of openness. The new challenge is to develop business intelligence in advance exploiting an adaptive approach. The idea is to learn business strategy once new negotiation model rise in the e-market arena. In this paper we present how recommendation technology may be deployed in an open negotiation environment where the interaction protocol models are not known in advance. The solution we propose is delivered as part of the ONE Platform, open source software that implements a fully distributed open environment for business negotiation.

Peer Recommendation Based on Text Mining Algorithm

Ask friends about a particular subject are a common situation in the daily life of a person. In virtual environments is a little more difficult. Virtual environments do not always allow face contact that helps people meet and share their experiences to answer questions about a particular subject. In this article are presented: a text mining method for obtaining the knowledge of people from forums and a recommender system that recommends people to ask them about a particular subject.

Discovering Good Sources for Recommender Systems

Discovering user knowledge is a key issue in recommender systems and many algorithms and techniques have been used in the attempt. One of the most critical problems in recommender systems is the lack of information, referred to as Cold Start and Sparsity problems. Research works have shown how to take advantage of additional databases with information about users [1], but they do not solve the new problem that arises: which relevant database to use? This paper contributes to that solution with a novel method for selecting information sources in the belief that they will be relevant and will result in better recommendations. We describe a new approach to explore and discover relevant information sources in order to obtain reliable knowledge about users. The relation between the improvement of the recommendation results and the sources selected based on these characteristics is shown by experiments selecting source based on their relevance and trustworthiness.

The User's Human Values Scale Methodology in Recommender Systems from Several Information Sources of the Organization

The main objective of this paper aims at developing a methodology that takes into account the human factor extracted from the several information sources of the organization of the recommender systems. This methodology is capable of extracting the Human Values Scale from the user, with reference to his/her features, in order to improve the adaptation of the Recommender Systems. This research is focused on the analysis of human values scale using the Portrait Values Questionnaire of Schwartz, which can take advantage of the several information sources of the organization through its attributes to define the methodology that response with more exactitude to preferences and interests of the user. This paper presents a demonstration of how the Human Values Scale of a user can be extracted from several information sources of the organization. A case study is presented to apply the methodology, in an effort to extract the user human values scale from bank domains.