Use of clustering for consideration set modeling in recommender systems

The cold-start problem has become a significant challenge in recommender systems. To solve this problem, most approaches use various user-side data and combine them with item-side information in their systems design. However, when such user data is not available, those methods become unfeasible. We provide a novel recommender system design approach which is based on two-stage decision heuristics. By utilizing only the item-side characteristics we first identify the structure of the final choice set and then generate it using stochastic and deterministic approaches

O lado comportamental dos agentes de recomendação : uma revisão bibliométrica

Recommendation agents have been used to assist consumers in online purchase for almost 20 years. Their use has been studied in academic research with two different approaches. The first one addresses computational problems related to generating accurate recommendations. The other seeks to understand how user interaction with recommendation agents can alter behaviors in online shopping. Through bibliometric and scientometric methods, this study looked for the most influential papers, authors and journals in the field of behavioral recommendation research. In the present work, only articles investigating behavioral aspects of recommendation usage were considered. The identified articles were analyzed in terms of their methodology, variables and repercussion. At the end, a total of 175 articles published in journals from many different fields of academic research were found, attesting the multidisciplinary nature of this topic. Most of the studies were empirical investigations using experimental methodology, however theoretical papers showed to be more influential. It was possible to identify 29 different dependent variables used to measure the effects of recommendations in online assisted purchase. The 19 independent variables used in these studies were related to characteristics of the recommendation agent, user characteristics or vendor characteristics. Results also showed that the field still lacks confirmatory studies capable of creating a greater assurance for the knowledge already developed in the field.um período de cerca de 20 anos. Sua utilização atualmente é estudada na pesquisa acadêmica a partir de duas diferentes abordagens. A primeira se destina à resolução de problemas computacionais relacionados à geração de recomendações acuradas. A segunda tem como intuito entender como a interação do usuário com agentes de recomendação pode alterar seu comportamento de compra online. Usando um método bibliométrico e cientométrico, este estudo buscou os artigos, autores e publicações mais influentes no campo de pesquisa comportamental. Isto significa que apenas artigos que investigaram aspectos comportamentais do uso de recomendações foram considerados. Os artigos identificados foram também analisados em termos de sua metodologia, variáveis e repercussão. A maioria dos estudos se tratavam de investigações empíricas usando metodologia experimental, entretanto os artigos teóricos se demonstraram mais influentes. Também foi possível identificar 29 variáveis dependentes usadas para medir os efeitos das recomendações em compras online assistidas. As 19 variáveis independentes usadas nesses estudos estavam relacionadas com características do agente de recomendação, características do usuário ou características do vendedor. Os resultados também demonstraram que o campo ainda carece de estudos confirmatórios capazes de criar mais certeza para o conhecimento já desenvolvido na área

A Recommender System for Online Consumer Reviews

Online consumer reviews have helped consumers to increase their knowledge about different products/services. While most previous studies try to provide general models that predict performance of online reviews, this study notes that different people look for different types of reviews. Hence, there is a need for developing a system that that is able to sort reviews differently for each user based on the ratings they previously assigned to other reviews. Using a design science approach, we address the above need by developing a recommender system that is able to predict the perceptions of each user regarding helpfulness of a specific review. In addition to addressing the sorting problem, this study also develops models that extract objective information from the text of online reviews including utilitarian cues, hedonic cues, product quality, service quality, price, and product comparison. Each of these characteristics may also be used for sorting and filtering online reviews

RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin

Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland

This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention

Model Blindness: Investigating a model-based route-recommender system’s impact on decision making

Model-Based Decision Support Systems (MDSS) are prominent in many professional domains of high consequence, such as aeronautics, emergency management, military command and control, healthcare, nuclear operations, intelligence analysis, and maritime operations. An MDSS generally uses a simplified model of the task and the operator to impose structure to the decision-making situation and provide information cues to the operator that is useful for the decision-making task. Models are simplifications, can be misspecified, and have errors. Adoption and use of these errorful models can lead to the impoverished decision-making of users. I term this impoverished state of the decision-maker model blindness. A series of two experiments were conducted to investigate the consequences of model blindness on human decision-making and performance and how those consequences can be mitigated via an explainable AI (XAI) intervention. The experiments implemented a simulated route recommender system as an MDSS with a true data-generating model (unobservable world model). In Experiment 1, the true model generating the recommended routes was misspecified to different levels to impose model blindness on users. In Experiment 2, the same route-recommender system was employed with a mitigation technique to overcome the impact of model-misspecifications on decision-making. Overall, the results of both experiments provide little support for performance degradation due to model blindness imposed by misspecified systems. The XAI intervention provided valuable insights into how participants adjusted their decision-making to account for bias in the system and deviated from choosing the model-recommended alternatives. The participants' decision strategies revealed that they could understand model limitations from feedback and explanations and could adapt their strategy to account for those misspecifications. The results provide strong support for evaluating the role of decision strategies in the model blindness confluence model. These results help establish a need for carefully evaluating model blindness during the development, implementation, and usage stages of MDSS.Ph.D

Conceptual Design Model of Computerized Personal-Decision AID (ComPDA)

To date, the attentions given to the improvement of decision support at organizational level has been enormous. On the contrary, academic research in improving the performance of computerized decision aid (CDA) for personal decision is lacking, in which some are dated. Nowadays, the existence of CDA which handles personal decision is mushrooming and progressively getting attention from users. Despite that, users’ perceptions of the suitable decision strategy and technique for CDA have not been subjected to systematic investigation. Literature reviews also indicate that most users do not go for complex mathematical techniques despite the fact that these techniques are better at handling the risks and uncertainties in decisions. In fact, more often than not, the development process of CDAs does not seem to adhere to any conceptual and theoretical model. In view of that, this study aims to propose a conceptual design model for computerized personal-decision aid (ComPDA). The following objectives are outlined to support the general aim: (i) to identify appropriate decision strategy and technique for ComPDA, (ii) to incorporate identified strategy and technique in the construction of conceptual design model for ComPDA (iii) to validate the conceptual design model in different situations via prototyping method and (iv) to measure the users’ perceived helpfulness of the ComPDA prototypes. Participatory design method was implemented in order to achieve objective i and ii. The findings were incorporated into the construction of the conceptual design model of ComPDA. In achieving objective iii, the conceptual design model was validated in two different case studies via prototyping: A- choosing development methodology in mobile computing course (md-Matrix); and B- purchasing a mobile phone (ep-Matrix). In achieving objective iv, an instrument (named as Q-HELP) was developed to measure the helpfulness (HLP) of the prototypes. This study identified four relevant constructs pertinent to helpfulness; reliability (REL), decision making effort (EFF), confidence (CON), and decision awareness (AWR). Altogether, 122 respondents participated where 63 were from case study A and 59 from case study B. Eight hypotheses were formulated comprising testing for correlation between all the constructs in Q-HELP with helpfulness, testing the average time spent to make a selection with and without the proposed ComPDA and testing if the mean score of helpfulness of the proposed ComPDA is high. Paired Samples t Test, Pearson Correlation analyses and descriptive analyses were utilized to validate the hypotheses. The results show that: REL and HLP are significantly correlated, EFF and HLP are significantly correlated, CON and HLP are significantly correlated, AWR and HLP are significantly correlated, the use of md-Matrix and ep-Matrix significantly reduces the time spent to make selection, mean score of helpfulness of md-Matrix is fairly high and mean score of helpfulness of ep-Matrix is high. However, it is concluded that the overall results exhibit sufficient indication that md-Matrix and ep-Matrix were found helpful to users in terms of reliability, lessening the decision making effort, increasing confidence and also awareness in decision making. This study has produced the following outcomes, along with achieving all of its objectives: (i) a conceptual design model for ComPDA which incorporates suitable decision strategies and techniques identified via systematic investigations; (ii) two functional ComPDA prototypes to validate the conceptual design model and to demonstrate its applicability in different situations, (iii) an instrument for measuring helpfulness which includes dimensions from outcome and process aspects; and (iv) comparative analyses of decision models, strategies and techniques which provide basis for future studies.