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UMUAI Tables of Contents: 1415161718192021222324

User Modeling and User-Adapted Interaction 24

Editors:Alfred Kobsa
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Links:link.springer.com | Table of Contents
  1. UMUAI 2014-02 Volume 24 Issue 1/2
  2. UMUAI 2014-08 Volume 24 Issue 3
  3. UMUAI 2014-10 Volume 24 Issue 4

UMUAI 2014-02 Volume 24 Issue 1/2

Preface to the special issue on context-aware recommender systems BIBAKFull-Text 1-5
  Gediminas Adomavicius; Dietmar Jannach
Recommender systems represent a popular area of personalization technologies that has enjoyed a tremendous amount of research and development activity in both academia and industry in the last 10--15 years. Recommender systems research typically explores and develops techniques and applications for recommending various products or services to individual users based on the knowledge of users' tastes and preferences as well as users' past activities (such as previous purchases), which are applicable in a variety of domains and settings (Jannach et al. 2010).
Keywords: Recommender systems; Context-awareness; Collaborative filtering
Experimental evaluation of context-dependent collaborative filtering using item splitting BIBAKFull-Text 7-34
  Linas Baltrunas; Francesco Ricci
Collaborative Filtering (CF) computes recommendations by leveraging a historical data set of users' ratings for items. CF assumes that the users' recorded ratings can help in predicting their future ratings. This has been validated extensively, but in some domains the user's ratings can be influenced by contextual conditions, such as the time, or the goal of the item consumption. This type of contextual information is not exploited by standard CF models. This paper introduces and analyzes a novel technique for context-aware CF called Item Splitting. In this approach items experienced in two alternative contextual conditions are "split" into two items. This means that the ratings of a split item, e.g., a place to visit, are assigned (split) to two new fictitious items representing for instance the place in summer and the same place in winter. This split is performed only if there is statistical evidence that under these two contextual conditions the items ratings are different; for instance, a place may be rated higher in summer than in winter. These two new fictitious items are then used, together with the unaffected items, in the rating prediction algorithm. When the system must predict the rating for that "split" item in a particular contextual condition (e.g., in summer), it will consider the new fictitious item representing the original one in that particular contextual condition, and will predict its rating. We evaluated this approach on real world, and semi-synthetic data sets using matrix factorization, and nearest neighbor CF algorithms. We show that Item Splitting can be beneficial and its performance depends on the method used to determine which items to split. We also show that the benefit of the method is determined by the relevance of the contextual factors that are used to split.
Keywords: Recommender Systems; Collaborative filtering; Context; Item splitting
Comparing context-aware recommender systems in terms of accuracy and diversity BIBAKFull-Text 35-65
  Umberto Panniello; Alexander Tuzhilin; Michele Gorgoglione
Although the area of context-aware recommender systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable "best bet" when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.
Keywords: Context-aware recommender systems; CARS; Pre-filtering; Post-filtering; Contextual modeling; Accuracy; Diversity; Performance measures
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols BIBAKFull-Text 67-119
  Pedro G. Campos; Fernando Díez; Iván Cantador
Exploiting temporal context has been proved to be an effective approach to improve recommendation performance, as shown, e.g. in the Netflix Prize competition. Time-aware recommender systems (TARS) are indeed receiving increasing attention. A wide range of approaches dealing with the time dimension in user modeling and recommendation strategies have been proposed. In the literature, however, reported results and conclusions about how to incorporate and exploit time information within the recommendation processes seem to be contradictory in some cases. Aiming to clarify and address existing discrepancies, in this paper we present a comprehensive survey and analysis of the state of the art on TARS. The analysis show that meaningful divergences appear in the evaluation protocols used -- metrics and methodologies. We identify a number of key conditions on offline evaluation of TARS, and based on these conditions, we provide a comprehensive classification of evaluation protocols for TARS. Moreover, we propose a methodological description framework aimed to make the evaluation process fair and reproducible. We also present an empirical study on the impact of different evaluation protocols on measuring relative performances of well-known TARS. The results obtained show that different uses of the above evaluation conditions yield to remarkably distinct performance and relative ranking values of the recommendation approaches. They reveal the need of clearly stating the evaluation conditions used to ensure comparability and reproducibility of reported results. From our analysis and experiments, we finally conclude with methodological issues a robust evaluation of TARS should take into consideration. Furthermore we provide a number of general guidelines to select proper conditions for evaluating particular TARS.
Keywords: Time-aware recommender systems; Context-aware recommender systems; Evaluation methodologies; Survey
Hybreed: A software framework for developing context-aware hybrid recommender systems BIBAKFull-Text 121-174
  Tim Hussein; Timm Linder; Werner Gaulke; Jürgen Ziegler
This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call dynamic contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed's functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.
Keywords: Recommender systems; Group recommendations; Context-aware recommendations; Distributed user models; Framework

UMUAI 2014-08 Volume 24 Issue 3

A case study of intended versus actual experience of adaptivity in a tangible storytelling system BIBAKFull-Text 175-217
  Karen Tanenbaum; Marek Hatala; Joshua Tanenbaum; Ron Wakkary; Alissa Antle
This article presents a case study of an adaptive, tangible storytelling system called "The Reading Glove". The research addresses a gap in the field of adaptivity for ubiquitous systems by taking a critical look at the notion of "adaptivity" and how users experience it. The Reading Glove is an interactive storytelling system featuring a wearable, glove-based interface and a set of narratively rich objects. A tabletop display provides adaptive recommendations which highlight objects to select next, functioning as an expert storytelling system. The recommendation engine can be run in three different configurations to examine the effects of different adaptive methods. The study of the design process as well as the user experience of the Reading Glove allows us to develop a deeper understanding of the experience of adaptivity that is useful for designers of intelligent systems, particularly those with ubiquitous and tangible forms of interaction.
Keywords: Adaptivity; Tangible computing; User models; Recommendation systems; Expert systems; User experience
A comparative study of collaboration-based reputation models for social recommender systems BIBAKFull-Text 219-260
  Kevin McNally; Michael P. O'Mahony; Barry Smyth
Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to identify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling user and item reputation in social recommender systems. In particular, we show how the various interactions between producers and consumers of content can be used to create so-called collaboration graphs, from which the reputation of users and items can be derived. We analyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recommending relevant pages to users based on the past experiences of search communities. By incorporating reputation into the existing HeyStaks recommendation framework, we demonstrate that the relevance of HeyStaks recommendations can be significantly improved based on data recorded during a live-user trial of the system.
Keywords: Reputation; Social recommender systems; Collaboration graphs

UMUAI 2014-10 Volume 24 Issue 4

Personalised viewing-time prediction in museums BIBAKFull-Text 263-314
  Fabian Bohnert; Ingrid Zukerman
People are often overwhelmed by the large amount of information provided in museum spaces, which makes it difficult for them to select exhibits of potential interest. As a first step in recommending exhibits where a visitor may wish to spend some time, this article investigates predictive user models for personalised prediction of museum visitors' viewing times at exhibits. We consider two content-based models and a nearest-neighbour collaborative filter, and develop a collaborative model based on the theory of spatial processes which relies on a notion of distance between exhibits. We discuss models of exhibit distance derived from viewing-time similarity, semantic similarity and walking distance. The results from our evaluation with a real-world dataset of visitor pathways collected at Melbourne Museum (Melbourne, Australia) suggest that utilising walking and semantic distances between exhibits enables more accurate predictions of a visitor's viewing times of unseen exhibits than using distances derived from observed exhibit viewing times. Our results also show that all models outperform a non-personalised baseline, that content-based viewing time prediction yields better results than nearest-neighbour collaborative prediction, and that our collaborative model based on spatial processes attains the highest predictive accuracy overall.
Keywords: Predictive user modelling; Content-based user models; Collaborative user models; Gaussian spatial processes; Cultural heritage
Identification of human implicit visual search intention based on eye movement and pupillary analysis BIBAKFull-Text 315-344
  Young-Min Jang; Rammohan Mallipeddi; Minho Lee
We propose a novel approach for the identification of human implicit visual search intention based on eye movement patterns and pupillary analysis, in general, as well as pupil size, gradient of pupil size variation, fixation length and fixation count corresponding to areas of interest, and fixation count corresponding to non-areas of interest, in particular. The proposed model identifies human implicit visual search intention as task-free visual browsing or task-oriented visual search. Task-oriented visual search is further identified as task-oriented visual search intent generation, task-oriented visual search intent maintenance, or task-oriented visual search intent disappearance. During a visual search, measurement of the pupillary response is greatly influenced by external factors such the intensity and size of the visual stimulus. To alleviate the effects of external factors, we propose a robust baseline model that can accurately measure the pupillary response. Graphical representation of the measured parameter values shows significant differences among the different intent conditions, which can then be used as features for identification. By using the eye movement patterns and pupillary analysis, we can detect the transitions between different implicit intentions -- task-free visual browsing intent to task-oriented visual search intent and task-oriented visual search intent maintenance to task-oriented visual search intent disappearance -- using a hierarchical support vector machine. In the proposed model, the hierarchical support vector machine is able to identify the transitions between different intent conditions with greater than 90% accuracy.
Keywords: Implicit intention detection; Task-free visual browsing intent; Task-oriented visual search intent; Intention recognition; Human computer interface & interaction; Pupillary analysis; Eye tracking; Pupil dilation