Collaborative and co-located information access is becoming increasingly common. However, fairly little attention has been devoted to the design of ubiquitous computing approaches for spontaneous exploration of large information spaces enabling co-located collaboration. We investigate whether an entity-based user interface provides a solution to support co-located search on heterogeneous devices. We present the design and implementation of QueryTogether, a multi-device collaborative search tool through which entities such as people, documents, and keywords can be used to compose queries that can be shared to a public screen or specific users with easy touch enabled interaction. We conducted mixed-methods user experiments with twenty seven participants (nine groups of three people), to compare the collaborative search with QueryTogether to a baseline adopting established search and collaboration interfaces. Results show that QueryTogether led to more balanced contribution and search engagement. While the overall s-recall in search was similar, in the QueryTogether condition participants found most of the relevant results earlier in the tasks, and for more than half of the queries avoided text entry by manipulating recommended entities. The video analysis demonstrated a more consistent common ground through increased attention to the common screen, and more transitions between collaboration styles. Therefore, this provided a better fit for the spontaneity of ubiquitous scenarios. QueryTogether and the corresponding study demonstrate the importance of entity based interfaces to improve collaboration by facilitating balanced participation, flexibility of collaboration styles and social processing of search entities across conversation and devices. The findings promote a vision of collaborative search support in spontaneous and ubiquitous multi-device settings, and better linking of conversation objects to searchable entities.

A significant fraction of information searches are motivated by the user’s primary task . An ideal search engine would be able to use information captured from the primary task to proactively retrieve useful information. Previous work has shown that many information retrieval activities depend on the primary task in which the retrieved information is to be used, but fairly little research has been focusing on methods that automatically learn the informational intents from the primary task context. We study how the implicit primary task context can be used to model the user’s search intent and to proactively retrieve relevant and useful information. Data comprising of logs from a user study, in which users are writing an essay, demonstrate that users’ search intents can be captured from the task and relevant and useful information can be proactively retrieved. Data from simulations with several datasets of different complexity show that the proposed approach of using primary task context generalizes to a variety of data. Our findings have implications for the design of proactive search systems that can infer users’ search intent implicitly by monitoring users’ primary task activities.

Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.

In difficult information seeking tasks, the majority of top-ranked documents for an initial query may be non-relevant, and negative relevance feedback may then help find relevant documents. Traditional negative relevance feedback has been studied on document results; we introduce a system and interface for negative feedback in a novel exploratory search setting, where continuous-valued feedback is directly given to keyword features of an inferred probabilistic user intent model. The introduced system allows both positive and negative feedback directly on an interactive visual interface, by letting the user manipulate keywords on an optimized visualization of modeled user intent. Feedback on the interactive intent model lets the user direct the search: Relevance of keywords is estimated from feedback by Bayesian inference, influence of feedback is increased by a novel propagation step, documents are retrieved by likelihoods of relevant versus non-relevant intents, and the most relevant keywords (having the highest upper confidence bounds of relevance) and the most non-relevant ones (having the smallest lower confidence bounds of relevance) are shown as options for further feedback. We carry out task-based information seeking experiments with real users on difficult real tasks; we compare the system to the nearest state of the art baseline allowing positive feedback only, and show negative feedback significantly improves the quality of retrieved information and user satisfaction for difficult tasks.

The user's understanding of information needs and the information available in the data collection can evolve during an exploratory search session. Search systems tailored for well-defined narrow search tasks may be suboptimal for exploratory search where the user can sequentially refine the expressions of her information needs and explore alternative search directions. A major challenge for exploratory search systems design is how to support such behavior and expose the user to relevant yet novel information that can be difficult to discover by using conventional query formulation techniques. We introduce IntentStreams, a system for exploratory search that provides interactive query refinement mechanisms and parallel visualization of search streams. The system models each search stream via an intent model allowing rapid user feedback. The user interface allows swift initiation of alternative and parallel search streams by direct manipulation that does not require typing. A study with 13 participants shows that IntentStreams provides better support for branching behavior compared to a conventional search system.

We present PromotionRank, a technique for generating a personalized ranking of grocery product promotions based on the contents of the customer’s personal shopping list. PromotionRank consists of four phases. First, information retrieval techniques are used to map shopping list items onto potentially relevant product categories. Second, since customers typically buy more items than what appear on their shopping lists, the set of potentially relevant categories is expanded using collaborative filtering. Third, we calculate a rank score for each category using a statistical interest criterion. Finally, the available promotions are ranked using the newly computed rank scores. To validate the different phases, we consider 12 months of anonymized shopping basket data from a large national supermarket. To demonstrate the effectiveness of PromotionRank, we also present results from two user studies. The first user study was conducted in a controlled setting using shopping lists of different lengths, whereas the second study was conducted within a large national supermarket using real customers and their personal shopping lists. The results of the two studies demonstrate that PromotionRank is able to identify promotions that are considered both relevant and interesting. As part of the second study, we used PromotionRank to identify relevant promotions to advertise and measure the influence of the advertisements on purchases. The results of this evaluation indicate that PromotionRank is also capable of targeting advertisements, improving sales compared to a baseline that selects random advertisements.

We present Ma$$iv€, an intelligent mobile grocery assistant that provides support for the customer during the entire shopping process. To guide the design of Ma$$iv€, we conducted a user study that explored customer preferences regarding features in a mobile grocery aid. We first describe the study and its results, after which we introduce the design principles and design of Ma$$iv€. We also describe the features that Ma$$iv€ supports and discuss functionalities that we are integrating into Ma$$iv€. As part of the discussion, we describe technical challenges that we have encountered during our development efforts.

We study the stable marriage problem in a distributed setting. The communication network is a bipartite graph, with men on one side and women on the other. Acceptable partners are connected by edges, and each participant has chosen a linear order on the adjacent nodes, indicating the matching preferences.

The fundamental nature of grocery shopping makes it an interesting domain for intelligent mobile assistants. Even though the central role of shopping lists is widely recognized, relatively little attention has been paid to facilitating shopping list creation and management. In this paper we introduce a predictive text input technique that is based on association rules and item frequencies. We also describe an interface design for integrating the predictive text input with a web-based mobile shopping assistant. In a user study we compared two interfaces, one with text input support and one without. Our results indicate that, even though shopping list entries are typically short, our technique makes text input significantly faster, decreases typing error rates and increases overall user satisfaction.

In a max-min LP, the objective is to maximise ω subject to Ax ≤ 1, Cx ≥ ω1, and x ≥ 0 for nonnegative matrices A and C. We present a local algorithm (constant-time distributed algorithm) for approximating max-min LPs. The approximation ratio of our algorithm is the best possible for any local algorithm; there is a matching unconditional lower bound.

In this paper we describe modifications to a natural language grocery retrieval system, introduced in our earlier work. We also compare our system against an off-the-shelf retrieval tool, and show that our system is significantly better for top-ranked retrieval results.

A local algorithm is a distributed algorithm where each node must operate solely based on the information that was available at system startup within a constant-size neighbourhood of the node. We study the applicability of local algorithms to max-min LPs where the objective is to maximise minkSigmav CkvXv subject to SigmavalphaivXv les 1 far each i and Xv ges 0 far each v. Here ckvges 0, and the support sets Vi= {v : alphaiv> 0}, Vk= {v : ckv> 0}, Iv= {i: alphaiv> 0} and Kv= {k : Ckv> 0} have bounded size. In the distributed setting, each agent v is responsible for choosing the value of Xv, and the communication network is a hypergraph H where the sets Vkand Viconstitute the hyperedges. We present inapproximability results for a wide range of structural assumptions; for example, even if |Vi| and |Vk| are bounded by some constants larger than 2, there is no local approximation scheme. To contrast the negative results, we present a local approximation algorithm which achieves good approximation ratios if we can bound the relative growth of the vertex neighbourhoods in H.

Widgets are embeddable objects that provide easy and ubiquitous access to dynamic information sources, for example weather, news or TV program information. Widgets are typically rather static - they provide the information regardless of whether the information is relevant to the user's current information needs. In this paper we introduce Capricorn, which is an intelligent interface for mobile widgets. The interface uses various adaptive web techniques for facilitating navigation. For example, we use collaborative filtering to recommend suitable widgets and we dim infrequently used widgets. The demonstration presents the Capricorn interface focusing on the adaptive parts of the interface. The user interface is web-based, and as such platform independent. However, our target environment is mobile phones, and thus the interface has been optimized for mobile phones.

Widgets are embeddable objects that provide easy and ubiquitous access to dynamic information sources, e.g., weather, news or TV program information. Interactions with widgets take place through a so-called widget engine, which is a specialized client-side runtime component that also provides functionalities for managing widgets. As the number of supported widgets increases, managing widgets becomes increasingly complex. For example, finding relevant or interesting widgets becomes difficult and the user interface easily gets cluttered with irrelevant widgets. In addition, interacting with information sources can be cumbersome, especially on mobile platforms. In order to facilitate widget management and interactions, we have developed Capricorn, an intelligent user interface that integrates adaptive navigation techniques into a widget engine. This paper describes the main functionalities of Capricorn and presents the results of a usability evaluation that measured user satisfaction and compared how user satisfaction varies between desktop and mobile platforms.

We introduce a grocery retrieval system that maps shopping lists written in natural language into actual products in a grocery store. We have developed the system using nine months of shopping basket data from a large Finnish supermarket. To evaluate the system, we used 70 real shopping lists gathered from customers of the supermarket. Our system achieves over 80% precision for products at rank one, and the precision is around 70% for products at rank 5.

We consider the problem of maximizing the lifetime of a given multicast connection in a wireless network of energy-constrained (e.g., battery-operated) nodes, by choosing ideal transmission power levels for the nodes relaying the connection. We distinguish between two basic operating modes: In a static power assignment, the power levels of the nodes are set at the beginning and remain unchanged until the nodes are depleted of energy. In a dynamic power schedule, the powers can be adjusted during operation. We show that while lifetime-maximizing static power assignments can be found in polynomial time, for dynamic schedules the problem becomes NP-hard. We introduce two approximation heuristics for the dynamic case, and experimentally verify that the lifetime of a dynamically adjusted multicast connection can be made several times longer than what can be achieved by the best possible static assignment.

This study presents a shared document-based annotation tool, EDUCOSM. Usefulness of the system is empirically evaluated in a real-life collaborative learning context. Relationships between learner's self-rated use of learning strategies, cognitive outcomes, and completion of various tasks in the system are investigated. An empirical study (n=31) was conducted in order to investigate various dependencies between variables from precourse self-rated questionnaire, system log file data collected during the course and postcourse e-mail survey.

In spite of advances in educational technology, most Web-based computer science courses rely on costly pre-made learning materials. By shifting the emphasis to peer-to-peer learning and other student-centred learning principles, more meaningful learning process can be accomplished, without preparing stand-alone Web-courses. A course applying these principles is presented along with a tool built for the purpose. The tool offers the possibility to build a joint information pool and publish new work while constructing knowledge by collaborative annotation of the information or published work. According to our experince, transparency in the learning process is also well-accepted and viewed beneficial by the students.

We prove that it is an NP-hard problem to determine the attraction radius of a stable vector in a binary Hopfield memory network, and even that the attraction radius is hard to approximate. Under synchronous updating, the problems are already NP-hard for two-step attraction radii; direct (one-step) attraction radii can be computed in polynomial time.

We present a new associative memory model based on the Hamming memory, but where the winner-take-all network part is replaced by a layer of nodes with somewhat complex node functions. This new memory can produce output vectors with individual “don't know” bits. the simulations demonstrate that this memory model works appropriately. © 1992 John Wiley & Sons, Inc.

The convergence properties of Hamming memory networks are studied. It is shown how to construct the network so that it probably converges to an appropriate result, and a tight bound is given on the convergence time. The bound on the convergence time is largest when several stored vectors are at the minimum distance from the input vector. For random binary vectors, the probability for such situations to occur is not small. With a specific choice of parameter values, the worst-case convergence time is on the order of p ln (pn), where p is the memory capacity and n is the vector length. By allowing the connection weights to change during the computation, the convergence time can be decreased considerably.

The worst-case upper bound on the convergence time of Hopfield associative memories is improved to half of its previously known value. Also, the consequences of allowing ;don't know' bits in both the input and the output are considered.

The Hamming memory is discussed along with the convergence theorem. Extensions to the model are also described. It is shown that, by properly choosing the parameters, the Hamming memory network always converges to the correct solution, and a tight bound on the convergence time is given

A scheme is presented for translating high-level descriptions of conceptual hierarchies into a neural network representation. The intuitive semantics of a conceptual hierarchy is provided by a Bayesian net, and the neural network implementation provably approximates the behavior of this net under a stochastic simulation rule