A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

The other side of the social web: A taxonomy for social information access

The power of the modern Web, which is frequently called the Social Web or Web 2.0, is frequently traced to the power of users as contributors of various kinds of contents through Wikis, blogs, and resource sharing sites. However, the community power impacts not only the production of Web content, but also the access to all kinds of Web content. A number of research groups worldwide explore what we call social information access techniques that help users get to the right information using "collective wisdom" distilled from actions of those who worked with this information earlier. This invited talk offers a brief introduction into this important research stream and reviews recent works on social information access performed at the University of Pittsburgh's PAWS Lab lead by the author. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM)

The use of implicit evidence for relevance feedback in web retrieval

In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update the display according to the user's interaction. Our research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence. We examine the two variations in terms of both user opinion and search effectiveness

A personalized system for conversational recommendations

technical reportIncreased computing power and theWeb have made information widely accessible. In turn, this has encouraged the development of recommendation systems that help users find items of interest, such as books or restaurants. Such systems are more useful when they personalize themselves to each user?s preferences, thus making the recommendation process more efficient and effective. In this paper, we present a new type of recommendation system that carries out a personalized dialogue with the user. This system ? the Adaptive Place Advisor ? treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. The system incorporates a user model that contains item, attribute, and value preferences, which it updates during each conversation and maintains across sessions. The Place Advisor uses both the conversational context and the user model to retrieve candidate items from a case base. The system then continues to ask questions, using personalized heuristics to select which attribute to ask about next, presenting complete items to the user only when a few remain. We report experimental results demonstrating the effectiveness of user modeling in reducing the time and number of interactions required to find a satisfactory item

Adaptive User Interfaces for Intelligent E-Learning: Issues and Trends

Adaptive User Interfaces have a long history rooted in the emergence of such eminent technologies as Artificial Intelligence, Soft Computing, Graphical User Interface, JAVA, Internet, and Mobile Services. More specifically, the advent and advancement of the Web and Mobile Learning Services has brought forward adaptivity as an immensely important issue for both efficacy and acceptability of such services. The success of such a learning process depends on the intelligent context-oriented presentation of the domain knowledge and its adaptivity in terms of complexity and granularity consistent to the learner’s cognitive level/progress. Researchers have always deemed adaptive user interfaces as a promising solution in this regard. However, the richness in the human behavior, technological opportunities, and contextual nature of information offers daunting challenges. These require creativity, cross-domain synergy, cross-cultural and cross-demographic understanding, and an adequate representation of mission and conception of the task. This paper provides a review of state-of-the-art in adaptive user interface research in Intelligent Multimedia Educational Systems and related areas with an emphasis on core issues and future directions

A User Behavior Based Study on Search Engine Ranking

In this era of information explosion, finding convenient ways to get the desired information is becoming ever more vital today. With a review of the existing information retrieval and feedback technology, this paper puts forward a method to establish and update user profile model through obtaining user’s implicit feedbacks. The user’s explicit information is not a must. Instead, this method, with the implicit information acquired by observing the behaviors of the users when browsing web pages, establishes and updates the user profile model and thus reduces the workload.Keywords: Information retrieval?Implicit feedback?Relevance feedback; User profile mode

Why Machiavellianism Matters in Childhood: The Relationship Between Children's Machiavellian Traits and Their Peer Interactions in a Natural Setting

The current study investigated the association between Machiavellianism and children’s peer interactions in the playground using observational methods. Primary school children (N = 34; 17 female), aged 9 to 11 years, completed the Kiddie Mach scale and were observed in natural play during 39 recesses (average observed time = 11.70 hours) over a full school year. Correlations for boys revealed that Machiavellianism was related to more time engaging in direct and indirect aggression, being accepted into other peer groups, and accepting peers into their own social group. Correlations revealed that for girls, Machiavellianism was associated with lower levels of indirect aggression, less time being accepted into other groups and less time accepting and rejecting other children into their own group. This preliminary pilot study indicates that Machiavellianism is associated with children’s observed social behaviour and aims to promote future observational research in this area