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

Estudios de la transformación enzimática de derivados de adamantano con cepas de hongos de los géneros aspergillus y fusarium, y sus potenciales actividades biológica

La idea es presentar el proyecto a la comunidad universitaria, teniendo en cuenta que el mismo se desarrolla entre la Facultadde Ciencias de la Alimentación, Bioquímicas y Farmacéuticas y el área Química del Instituto de Ciencias Básicas de la Universidad Nacionalde San Juan.  Está dirigido a la obtención de compuestos novedosos a partir de sustratos orgánicos derivados de adamantano, a través de su funcionalización utilizando catalizadores biológicos (enzimas), producidos por microorganismos (hongos filamentosos). Los metabolitos obtenidos serán aislados, purificados y analizados por métodos espectroscópicos para determinar su estructura. Los compuestos puros obtenidos por esta técnica, serán evaluados in vitro en su actividad sobre la acción tóxica de NMDA midiendo viabilidad y muerte celular

La hoja de olea europaea L….¿Un recurso aprovechable en la industria farmacéutica?

Es de conocimiento que la provincia de San Juan reúne las condiciones necesarias para el cultivo económico de la planta de olivo, contando además con importantes organizaciones, instaladas en nuestra Facultad, dedicadas a la olivicultura. Es por ello y debido a la gran importancia que ha tomado en nuestra provincia el cultivo de dicha planta y sabiendo que la mayor parte de los estudios desde el aspecto científico se han enfocado hacia los beneficios del aceite y sus características, se ve necesario el inicio de investigaciones que nos conduzcan a estudiar la existencia de compuestos de interés en las partes menos analizadas del cultivo, como la foliar, con fines medicinales, farmacéuticos y agropecuarios

Comparing product specifications to solve the cold start problem in a recommender system

© 2016 IEEE. Recommender systems are widely used applications to solve the problems of information overload, usually on websites. A well-known problem of recommender systems is the problem of cold start, which is caused by the lack of data. A recommendation system can only produce good recommendations after it has accumulated enough data The problem becomes even more challenging when the recommender system comes to deal with new products or the products have not been evaluated by consumers. This paper addresses this problem based on a comparison of product specifications, experiments were conducted in the recommendation domain of digital cameras

Enhancement of infrequent purchased product recommendation using data mining techniques

International audienceRecommender Systems (RS) have emerged to help users make good decisions about which products to choose from the vast range of products available on the Internet. Many of the existing recommender systems are developed for simple and frequently purchased products using a collaborative filtering (CF) approach. This approach is not applicable for recommending infrequently purchased products, as no user ratings data or previous user purchase history is available. This paper proposes a new recommender system approach that uses knowledge extracted from user online reviews for recommending infrequently purchased products. Opinion mining and rough set association rule mining are applied to extract knowledge from user online reviews. The extracted knowledge is then used to expand a user's query to retrieve the products that most likely match the user's preferences. The result of the experiment shows that the proposed approach, the Query Expansion Matching-based Search (QEMS), improves the performance of the existing Standard Matching-based Search (SMS) by recommending more products that satisfy the user's needs

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 [1] 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. © 2006 IEEE

Presentación del Servicio de Psicología

Fil: Aciar, E.. Universidad Nacional de Cuyo. Facultad de OdontologíaFil: Martí, Sonia Ema. Universidad Nacional de Cuyo. Facultad de OdontologíaFil: Levinzon, G.. Universidad Nacional de Cuyo. Facultad de OdontologíaFil: Domingo, S.. Universidad Nacional de Cuyo. Facultad de Odontologí

Effective product recommendation using the real-time web

Paper presented at the Thirtieth SGAI International Conference on Artificial Intelligence (AI-2010), 14-16 December 2010, Cambridge, England, UKThe so-called real-time web (RTW) is a web of opinions, comments, and personal viewpoints, often expressed in the form of short, 140-character text messages providing abbreviated and highly personalized commentary in real-time. Today, Twitter is undoubtedly the king of the RTW. It boasts 190 million users and generates in the region of 65m tweets per day. This RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research but it is useful to consider its applicability to recommendation scenarios. In this paper we consider harnessing the real-time opinions of users, expressed through the Twitter-like short textual reviews available on the Blippr service (www.blippr.com). In particular we describe how users and products can be represented from the terms used in their associated reviews and describe experiments to highlight the recommendation potential of this RTW data-source and approach.Science Foundation IrelandEmbargo until January 2012 - AV 8/2/201