Comparison of group recommendation algorithms

In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops. Although most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not intended for personal usage but rather for consumption in groups. This paper investigates how effective group recommendations for movies can be generated by combining the group members' preferences (as expressed by ratings) or by combining the group members' recommendations. These two grouping strategies, which convert traditional recommendation algorithms into group recommendation algorithms, are combined with five commonly used recommendation algorithms to calculate group recommendations for different group compositions. The group recommendations are not only assessed in terms of accuracy, but also in terms of other qualitative aspects that are important for users such as diversity, coverage, and serendipity. In addition, the paper discusses the influence of the size and composition of the group on the quality of the recommendations. The results show that the grouping strategy which produces the most accurate results depends on the algorithm that is used for generating individual recommendations. Therefore, the paper proposes a combination of grouping strategies which outperforms each individual strategy in terms of accuracy. Besides, the results show that the accuracy of the group recommendations increases as the similarity between members of the group increases. Also the diversity, coverage, and serendipity of the group recommendations are to a large extent dependent on the used grouping strategy and recommendation algorithm. Consequently for (commercial) group recommender systems, the grouping strategy and algorithm have to be chosen carefully in order to optimize the desired quality metrics of the group recommendations. The conclusions of this paper can be used as guidelines for this selection process

UK Research Information Shared Service (UKRISS) Final Report, July 2014

The reporting of research information is a complex and expensive activity for research organisations (ROs). There is little alignment between funders of the reporting requests made to institutions and requests made to individual researchers about their research outputs and outcomes. This inevitably results in duplication and increased costs across the sector, whilst limiting the potential sharing and reuse of the information. The UK Research Information Shared Service (UKRISS) project conducted a feasibility and scoping study for the reporting of research information at a national level based on CERIF (Common European Research Information Format), with the objective of increasing efficiency, productivity and quality across the sector. The aim was to define and prototype solutions which are compelling, easy to use, have a low entry barrier, and support innovative information sharing and benchmarking. CERIF has emerged as the preferred format for expressing research information across Europe. To date, CERIF has been piloted for specific applications, but not as a format for reporting requirements across all UK ROs. The final report presents the work carried out by the UKRISS project, including requirements gathering, modelling and prototyping, as well as recommendation for sustainability. UKRISS was divided into two phases. Phase 1, mapping the reporting landscape, ran from March 2012 to December 2012. Phase 2, exploring delivery of potential solutions, began in February 2013 and ended in December 2013

From Group Recommendations to Group Formation

There has been significant recent interest in the area of group recommendations, where, given groups of users of a recommender system, one wants to recommend top-k items to a group that maximize the satisfaction of the group members, according to a chosen semantics of group satisfaction. Examples semantics of satisfaction of a recommended itemset to a group include the so-called least misery (LM) and aggregate voting (AV). We consider the complementary problem of how to form groups such that the users in the formed groups are most satisfied with the suggested top-k recommendations. We assume that the recommendations will be generated according to one of the two group recommendation semantics - LM or AV. Rather than assuming groups are given, or rely on ad hoc group formation dynamics, our framework allows a strategic approach for forming groups of users in order to maximize satisfaction. We show that the problem is NP-hard to solve optimally under both semantics. Furthermore, we develop two efficient algorithms for group formation under LM and show that they achieve bounded absolute error. We develop efficient heuristic algorithms for group formation under AV. We validate our results and demonstrate the scalability and effectiveness of our group formation algorithms on two large real data sets.Comment: 14 pages, 22 figure

State Strategies to Improve Quality and Efficiency: Making the Most of Opportunities in National Health Reform

Examines ten states' initiatives to address key components of quality and efficiency improvement, including data collection, aggregation, and standardization; public reporting; payment reform; consumer engagement; and provider engagement

Synthesis and final recommendations on the development of a European Information System for Organic Markets. = Deliverable D6 of the European Project EISfOM QLK5-2002-02400

Executive summary European markets for organic products are growing rapidly, but the market information available in most European countries is woefully inadequate. Often only very basic data such as certified organic holdings and land area are reported, and sometimes not even individual crop areas or livestock numbers. Important market data, such as the amount of production, consumption, international trade or producer and consumer prices, do not exist in most European countries. In some European countries there are only rough estimates of the levels of production and consumption. There is no standardisation and data are seldom comparable. Furthermore, detailed information on specific commodities is missing. Hence, investment decisions are taken under conditions of great uncertainty. Policy evaluation, including periodic monitoring of the European Action Plan for Organic Food and Farming and RDP 2007-2013, will require many other data in addition to those regarding production structures and financial data that are already available, but obtaining this information would require a new EU-wide data collection and processing system (DCPS) to be put in place. The European Information System for Organic Markets (EISfOM) project is an EUfunded Concerted Action which has analysed and documented the current situation and proposed ways in which organic data collection and processing systems (DCPS) can be improved by means of: • improvement in the current situation of data collecting and processing systems for the organic sector • innovation in data collection and processing systems for the organic sector • integration of conventional and organic data collection and processing systems This report summarises the most relevant findings of the EISfOM project, which are analysed in the main project reports: Wolfert, S., Kramer, K. J., Richter, T., Hempfling, G., Lux. S. and Recke, G. (eds.) (2004). Review of data collection and processing systems for organic and conventional markets. EISfOM (QLK5-2002-02400) project deliverable submitted to European Commission. www.eisfom.org/publications. Recke, G., Hamm, U., Lampkin, N., Zanoli, R., Vitulano, S. and Olmos, S. (eds.) (2004a) Report on proposals for the development, harmonisation and quality assurance of organic data collection and processing systems (DCPS). EISfOM (QLK5-2002-02400) project deliverable submitted to European Commission. www.eisfom.org/publications. Recke, G., Willer, H., Lampkin, N. and Vaughan, A. (eds.) (2004b). Development of a European Information System for Organic Markets – Improving the Scope and Quality of Statistical Data. Proceedings of the 1st EISfOM European Seminar, Berlin, Germany, 26-27 April, 2004. Research Institute of Organic Agriculture (FiBL), Frick, Switzerland. www.eisfom.org/publications. Gleirscher, N., Schermer, M., Wroblewska, M. and Zakowska-Biemans, S. (2005) Report on the evaluation of the pilot case studies. EISfOM (QLK5-2002-02400) project deliverable submitted to European Commission. www.eisfom.org/publications. QLK5-2002-02400 European Information System for Organic Markets (EISfOM) D6 final report Rippin, M. and Lampkin, N. (eds.) (2005) Framework for a European Information System for Organic Markets. Unpublished report of the project European Information System for Organic Markets (EISfOM) (QLK5-2002-02400). Rippin, M., Willer, H., Lampkin, N., and Vaughan A. (2006). Towards a European Framework for Organic Market information, Proceedings of the 2nd EISfOM European Seminar, Brussels, November 10 and 11, 2005. Research Institute of Organic Agriculture (FiBL), Frick, Switzerland. www.eisfom.org/publications

Individual and Domain Adaptation in Sentence Planning for Dialogue

One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations