Recommendation Scenarios Research Articles

The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation

A recommender system suggests items to users by predicting what might be interesting for them. The prediction task has been highlighted in the literature as the most important one computed by a recommender system. Its role becomes even more central when a system works with groups, since the predictions might be built for each user or for the whole group. This paper presents a deep evaluation of three approaches, used for the prediction of the ratings in a group recommendation scenario in which groups are detected by clustering the users. Experimental results confirm that the approach to predict the ratings strongly influences the performance of a system and show that building predictions for each user, with respect to building predictions for a group, leads to great improvements in the accuracy of the recommendations.

Collaborative Filtering beyond the User-Item Matrix

Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.

Analyzing, Detecting, and Exploiting Sentiment in Web Queries

The Web contains an increasing amount of biased and opinionated documents on politics, products, and polarizing events. In this article, we present an indepth analysis of Web search queries for controversial topics, focusing on query sentiment. To this end, we conduct extensive user assessments and discriminative term analyses, as well as a sentiment analysis using the SentiWordNet thesaurus, a lexical resource containing sentiment annotations. Furthermore, in order to detect the sentiment expressed in queries, we build different classifiers based on query texts, query result titles, and snippets. We demonstrate the virtue of query sentiment detection in two different use cases. First, we define a query recommendation scenario that employs sentiment detection of results to recommend additional queries for polarized queries issued by search engine users. The second application scenario is controversial topic discovery, where query sentiment classifiers are employed to discover previously unknown topics that trigger both highly positive and negative opinions among the users of a search engine. For both use cases, the results of our evaluations on real-world data are promising and show the viability and potential of query sentiment analysis in practical scenarios.

Offline optimization for user-specific hybrid recommender systems

User-specific hybrid recommender systems aim at harnessing the power of multiple recommendation algorithms in a user-specific hybrid scenario. While research has previously focused on self-learning hybrid configurations, such systems are often too complex to take out of the lab and are seldom tested against real-world requirements. In this work, we describe a self-learning user-specific hybrid recommender system and assess its ability towards meeting a set of pre-defined requirements relevant to online recommendation scenarios: responsiveness, scalability, system transparency and user control. By integrating a client-server architectural design, the system was able to scale across multiple computing nodes in a very flexible way. A specific user-interface for a movie recommendation scenario is proposed to illustrate system transparency and user control possibilities, which integrate directly in the hybrid recommendation process. Finally, experiments were performed focusing both on weak and strong scaling scenarios on a high performance computing environment. Results showed performance to be limited only by the slowest integrated recommendation algorithm with very limited hybrid optimization overhead.

Central Aortic Blood Pressure and Management of Hypertension

See related article, pp 1138–1145 Hypertension, a condition of elevated arterial blood pressure (BP) conventionally diagnosed by brachial cuff sphygmomanometry, is associated with increased risk of cardiovascular mortality and morbidity and end-organ damage. However, the marked differences in pulse pressure between the central aorta and peripheral limbs1 suggest that effects of peak values of arterial BP (eg, systolic BP) on centrally located organs (heart, brain, kidney) may not be accurately assessed using peripheral measurements. Our early studies2 showed substantial difference in the effects of sublingual nitroglycerin on peripheral and central (carotid) pulse pressures: in some cases, central systolic BP decreasing ≤20 mm Hg with little or no effect on brachial or radial systolic BP. There were, however, marked changes in the pulse wave form.2 The relationship between central aortic and radial pressure waves, quantified in terms of a mathematical transfer function, has been validated to be applicable across a large range of physiological pressures.3 The use of this noninvasive technique (and other variations, including other forms of analysis of the radial pulse or direct registration of the carotid pulse) has facilitated a large number of studies highlighting the differential effects of antihypertensive therapy on central aortic systolic BP for similar values of brachial cuff systolic BP.4 A seminal study by McEniery et al5 in >10 000 subjects demonstrated a substantial overlap of central and brachial BP between categories of hypertension. Approximately 32% of men and 10% of women who would be considered to have normal brachial systolic BP (and therefore, not treated) would be classified as having stage 1 hypertension based on equivalent central aortic systolic BP. Indeed, the implications of these findings suggested a possible sign of a paradigm shift in the management and treatment of hypertension as a significant cardiovascular risk.6 Subsequent studies …

Recommender system in collaborative learning environment using an influence diagram

Giving useful recommendations to students to improve collaboration in a learning experience requires tracking and analyzing student team interactions, identifying the problems and the target student. Previously, we proposed an approach to track students and assess their collaboration, but it did not perform any decision analysis to choose a recommendation for the student. In this paper, we propose an influence diagram, which includes the observable variables relevant for assessing collaboration, and the variable representing whether the student collaborates or not. We have analyzed the influence diagram with two machine learning techniques: an attribute selector, indicating the most important attributes that the model uses to recommend, and a decision tree algorithm revealing four different scenarios of recommendation. These analyses provide two useful outputs: (a) an automatic recommender, which can warn of problematic circumstances, and (b) a pedagogical support system (decision tree) that provides a visual explanation of the recommendation suggested.

Nontrivial landmark recommendation using geotagged photos

Online photo-sharing sites provide a wealth of information about user behavior and their potential is increasing as it becomes ever-more common for images to be associated with location information in the form of geotags. In this article, we propose a novel approach that exploits geotagged images from an online community for the purpose of personalized landmark recommendation. Under our formulation of the task, recommended landmarks should be relevant to user interests and additionally they should constitute nontrivial recommendations. In other words, recommendations of landmarks that are highly popular and frequently visited and can be easily discovered through other information sources such as travel guides should be avoided in favor of recommendations that relate to users' personal interests. We propose a collaborative filtering approach to the personalized landmark recommendation task within a matrix factorization framework. Our approach, WMF-CR, combines weighted matrix factorization and category-based regularization. The integrated weights emphasize the contribution of nontrivial landmarks in order to focus the recommendation model specifically on the generation of nontrivial recommendations. They support the judicious elimination of trivial landmarks from consideration without also discarding information valuable for recommendation. Category-based regularization addresses the sparse data problem, which is arguably even greater in the case of our landmark recommendation task than in other recommendation scenarios due to the limited amount of travel experience recorded in the online image set of any given user. We use category information extracted from Wikipedia in order to provide the system with a method to generalize the semantics of landmarks and allow the model to relate them not only on the basis of identity, but also on the basis of topical commonality. The proposed approach is computational scalable, that is, its complexity is linear with the number of observed preferences in the user-landmark preference matrix and the number of nonzero similarities in the category-based landmark similarity matrix. We evaluate the approach on a large collection of geotagged photos gathered from Flickr. Our experimental results demonstrate that WMF-CR outperforms several state-of-the-art baseline approaches in recommending nontrivial landmarks. Additionally, they demonstrate that the approach is well suited for addressing data sparseness and provides particular performance improvement in the case of users who have limited travel experience, that is, have visited only few cities or few landmarks.

Introduction to special section on CAMRa2010

The challenge and workshop on Context-Aware Movie Recommendation (CAMRa2010) were conducted jointly in 2010 with the Recommender Systems conference. The challenge focused on three context-aware recommendation scenarios: time-based, mood-based, and social recommendation. The participants were provided with anonymized datasets from two real-world online movie recommendation communities and competed against each other for obtaining the highest accuracy of recommendations. The datasets contained contextual features, such as tags, annotation, social relationsips, and comments, normally not available in public recommendation datasets. More than 40 teams from 21 countries participated in the challenge. Their participation was summarized by 10 papers published by the workshop, which have been extended and revised for this special section. In this preface we overview the challenge datasets, tasks, evaluation metrics, and the obtained outcomes.

CosTriage: A Cost-Aware Triage Algorithm for Bug Reporting Systems

"Who can fix this bug?" is an important question in bug triage to "accurately" assign developers to bug reports. To address this question, recent research treats it as a optimizing recommendation accuracy problem and proposes a solution that is essentially an instance of content-based recommendation (CBR). However, CBR is well-known to cause over-specialization, recommending only the types of bugs that each developer has solved before. This problem is critical in practice, as some experienced developers could be overloaded, and this would slow the bug fixing process. In this paper, we take two directions to address this problem: First,we reformulate the problem as an optimization problem of both accuracy and cost. Second, we adopt a content-boosted collaborative filtering (CBCF), combining an existing CBR with a collaborative filtering recommender (CF), which enhances the recommendationquality of either approach alone. However, unlike general recommendation scenarios, bug fix history is extremely sparse. Due to the nature of bug fixes, one bug is fixed by only one developer, which makes it challenging to pursue the above two directions. To address this challenge, we develop a topic-model to reduce the sparseness and enhance the quality of CBCF. Our experimental evaluation shows that our solution reduces the cost efficiently by 30% without seriously compromising accuracy.

Modeling and understanding communities in online social

The amount of multimedia content is on a constant increase, and people interact with each other and with content on a daily basis through social media systems. The goal of this thesis was to model and understand emerging online communities that revolve around multimedia content, more specifically photos, by using large-scale data and probabilistic models in a quantitative approach. The disertation has four contributions. First, using data from two online photo management systems, this thesis examined different aspects of the behavior of users of these systems pertaining to the uploading and sharing of photos with other users and online groups. Second, probabilistic topic models were used to model online entities, such as users and groups of users, and the new proposed representations were shown to be useful for further understanding such entities, as well as to have practical applications in search and recommendation scenarios. Third, by jointly modeling users from two different social photo systems, it was shown that differences at the level of vocabulary exist, and different sharing behaviors can be observed. Finally, by modeling online user groups as entities in a topic-based model, hyper-communities were discovered in an automatic fashion based on various topic-based representations. These hyper-communities were shown, both through an objective and a subjective evaluation with a number of users, to be generally homogeneous, and therefore likely to constitute a viable exploration technique for online communities.

Collaborative filtering based on workflow space

The traditional recommender systems are usually oriented to general situations in daily lives (e.g. recommend movies, books, music, news and etc.), but seldom cover the recommendation scenarios for the collaborative team environments. We have done an explorative study on collaborative filtering mechanism for collaborative team environments, which is some kind of multi-dimensional recommender systems problem with consideration of workflow context. This paper proposed 3-dimensional workflow space model, and investigated the new similarities measure between members in workflow space. Then, the new similarities measure is utilized into collaborative filtering for recommender systems in collaborative team environments. At last, the efficiency and usability of the proposed method are validated by experiments referring to a real-world collaborative team of a manufacturing enterprise.

Physician Perspectives to Inform a New Recommendation for Meningococcal Conjugate Vaccine (MCV4)

Purpose In January 2005, the U.S. Food and Drug Administration licensed a new tetravalent meningococcal conjugate vaccine (MCV4). Before any policy decisions by the U.S. Advisory Committee on Immunization Practices (ACIP) related to MCV4, the Centers for Disease Control and Prevention requested a study to explore the perspective of primary care physicians regarding different recommendation scenarios for use of MCV4. Methods Cross-sectional mail survey of a national random sample of pediatricians (PDs) and family physicians (FPs), conducted January 2005. Respondents chose from four MCV4 recommendation scenarios in terms of ability to implement, perceived patient/parent preferences, scientific evidence, and overall best fit. Results Response rate to the single-mailing survey was 57%. In terms of ability to implement, respondents generally preferred an MCV4 recommendation targeted to middle-school entry (11–12 years old) or with the Td booster at any age, but on the basis of scientific evidence they favored MCV4 at high school completion. For “overall best fit,” relatively equal proportions of respondents favored a recommendation at middle school entry and one linked to Td booster administration (whenever it occurred); there were no significant differences between PDs and FPs. Major influences on willingness to recommend MCV4 were vaccine safety/side effects and insurance coverage/reimbursement. Conclusions Support for an MCV4 recommendation at middle school entry is common but not universal among primary care providers. Data suggest that respondents appreciate the potential discrepancy between practical aspects of vaccine delivery and the need to protect those adolescents at greatest risk of disease. Respondents’ preferences for the overall best fit appear to prioritize ease of implementation over epidemiologic patterns.