Informal Online Decision Making: Current Practices and Support System Design

Existing group decision support systems are too complex to support lightweight, informal decision making made popular by the amount of information available on the Web. From an examination of related work, an online survey and a formative study to examine how people currently use the Web for decision support, we present a set of design recommendations towards the development of an informal Web decision support tool

Web Science: expanding the notion of Computer Science

Academic disciplines which practice in the context of rapid external change face particular problems when seeking to maintain timely, current and relevant teaching programs. In different institutions faculty will tune and update individual component courses while more radical revisions are typically departmental-wide strategic responses to perceived needs. Internationally, the ACM has sought to define curriculum recommendations since the 1960s and recognizes the diversity of the computing disciplines with its 2005 overview volume. The consequent rolling program of revisions is demanding in terms of time and effort, but an inevitable response to the change inherent is our family of specialisms. Preparation for the Computer Curricula 2013 is underway, so it seems appropriate to ask what place Web Science will have in the curriculum landscape. Web Science has been variously described; the most concise definition being the ‘science of decentralized information systems’. Web science is fundamentally interdisciplinary encompassing the study of the technologies and engineering which constitute the Web, alongside emerging associated human, social and organizational practices. Furthermore, to date little teaching of Web Science is at undergraduate level. Some questions emerge - is Web Science a transient artifact? Can Web Science claim a place in the ACM family, Is Web Science an exotic relative with a home elsewhere? This paper discusses the role and place of Web Science in the context of the computing disciplines. It provides an account of work which has been established towards defining an initial curriculum for Web Science with plans for future developments utilizing novel methods to support and elaborate curriculum definition and review. The findings of a desk survey of existing related curriculum recommendations are presented. The paper concludes with recommendations for future activities which may help us determine whether we should expand the notion of computer science

Exploiting Synergy Between Ontologies and Recommender Systems

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured

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

User evaluation of a market-based recommender system

Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommende