Another example is items to users based on their historical ratings. In that, although many people don't like animations, they may real-world scenarios, user interests may drift over still have interests in emerging 3-D animations because of the time since they are affected by moods, contexts, fantastic 3-D visual effects. These observations show that, and pop culture trends. This leads to the fact that although many aspects of user interests can be found based a user's historical ratings comprise many aspects of on users' historical ratings, at a certain time slice, one user's user interests spanning a long time period. However, interest may only focus on one or a couple of aspects. Thus, at a certain time slice, one user's interest may the static CF methods built on the entire historical ratings are only focus on one or a couple of aspects. Thus, inadequate to capture user-interest drift. In order to track user CF techniques based on the entire historical ratings interests and create comprehensive user profiles such that different may recommend inappropriate items. In this paper, recommendation strategies can be used for consistenttaste we consider modeling user-interest drift over time users and changing-taste users, a CF method that can based on the assumption that each user has multiple model user interests over time is required.

Given the number of online sources for news, the volumes of news generated are so daunting that gaining insight from these collections become impossible without some aid to link them. Semantic linking of news articles facilitates grouping of similar or relevant news stories together for ease of human consumption. For example, a political analyst may like to have a single view of all news articles that report visits of State heads of different countries to a single country to make an in-depth analytical report on the possible impacts of all associated events. It is likely that no news source links all the relevant news together. In this paper, we discuss a multi-resolution, multi-perspective news analysis system that can link news articles collected from diverse sources over a period of time. The distinctive feature of the proposed news linking system is its capability to simultaneously link news articles and stories at multiple levels of granularity. At the lowest level several articles reporting the same event are linked together. Higher level groupings are more contextual and semantic. We have deployed a range of algorithms that use statistical text processing and Natural Language Processing techniques. The system is incremental in nature and depicts how stories have evolved over time along with main actors and activities. It also illustrates how a single story diverges into multiple themes or multiple stories converge due to conceptual similarity. Accuracy of linking thematically and conceptually linked news articles are also presented.

We have developed a novel hybrid representation for Music Information Retrieval. Our representation is built by incorporating audio content into the tag space in a tag-track matrix, and then learning hybrid concepts using latent semantic analysis. We apply this representation to the task of music recommendation, using similarity-based retrieval from a query music track. We also develop a new approach to evaluating music recommender systems, which is based upon the relationship of users liking tracks. We are interested in measuring the recommendation quality, and the rate at which cold-start tracks are recommended. Our hybrid representation is able to outperform a tag-only representation, in terms of both recommendation quality and the rate that cold-start tracks are included as recommendations.

Robots depend on captured images for perceiving the environment. A robot can replace a human in capturing quality photographs for publishing. In this paper, we employ an iterative photo capture by robots (by repositioning itself) to capture good quality photographs. Our image quality assessment approach is based on few high level features of the image combined with some of the aesthetic guidelines of professional photography. Our system can also be used in web image search applications to rank images. We test our quality assessment approach on a large and diversified dataset and our system is able to achieve a classification accuracy of 79%. We assess the aesthetic error in the captured image and estimate the change required in orientation of the robot to retake an aesthetically better photograph. Our experiments are conducted on NAO robot with no stereo vision. The results demonstrate that our system can be used to capture professional photographs which are in accord with the human professional photography.

The paper introduces a logical framework for negotiation among dishonest agents. The framework relies on the use of abductive logic programming as a knowledge representation language for agents to deal with incomplete information and preferences. The paper shows how intentionally false or inaccurate information of agents could be encoded in the agents' knowledge bases. Such disinformation can be effectively used in the process of negotiation to have desired outcomes by agents. The negotiation processes are formulated under the answer set semantics of abductive logic programming and enable the exploration of various strategies that agents can employ in their negotiation

Many systems use Markov models to generate finite-length sequences that imitate a given style. These systems often need to enforce specific control constraints on the sequences to generate. Unfortunately, control constraints are not compatible with Markov models, as they induce long-range dependencies that violate the Markov hypothesis of limited memory. Attempts to solve this issue using heuristic search do not give any guarantee on the nature and probability of the sequences generated. We propose a novel and efficient approach to controlled Markov generation for a specific class of control constraints that 1) guarantees that generated sequences satisfy control constraints and 2) follow the statistical distribution of the initial Markov model. Revisiting Markov generation in the framework of constraint satisfaction, we show how constraints can be compiled into a non-homogeneous Markov model, using arc-consistency techniques and renormalization. We illustrate the approach on a melody generation problem and sketch some realtime applications in which control constraints are given by gesture controllers.

Identifying which outlet in social media leads the rest in disseminating novel information on specific topics is an interesting challenge for information analysts and social scientists. In this work, we hypothesize that novel ideas are disseminated through the creation and propagation of new or newly emphasized key words, and therefore lead/lag of outlets can be estimated by tracking word usage across these outlets. First, we demonstrate the validaty of our hypothesis by showing that a simple TF-IDF based nearest-neighbors approach can recover generally accepted lead/lag behavior on the outlets pair of ACM journal articles and conference papers. Next, we build a new topic model called LeadLag LDA that estimates the lead/lag of the outlets on specific topics. We validate the topic model using the lead/lag results from the TF-IDF nearest neighbors approach. Finally, we present results from our model on two different outlet pairs of blogs vs. news media and grant proposals vs. research publications that reveal interesting patterns.

Internet memes are phenomena that rapidly gain popularity or notoriety on the Internet. Often, modifications or spoofs add to the profile of the original idea thus turning it into a phenomenon that transgresses social and cultural boundaries. It is commonly assumed that Internet memes spread virally but scientific evidence as to this assumption is scarce. In this paper, we address this issue and investigate the epidemic dynamics of 150 famous Internet memes. Our analysis is based on time series data that were collected from Google Insights, Delicious, Digg, and StumbleUpon. We find that differential equation models from mathematical epidemiology as well as simple log-normal distributions give a good account of the growth and decline of memes. We discuss the role of log-normal distributions in modeling Internet phenomena and touch on practical implications of our findings.

Social media, such as blogs, are often seen as democratic entities that allow more voices to be heard than the conventional mass or elite media. Some also feel that social media exhibits a balancing force against the arguably slanted elite media. A systematic comparison between social and mainstream media is necessary but challenging due to the scale and dynamic nature of modern communication. Here we propose empirical measures to quantify the extent and dynamics of social (blog) and mainstream (news) media bias. We focus on a particular form of bias--coverage quantity--as applied to stories about the 111th US Congress. We compare observed coverage of Members of Congress against a null model of unbiased coverage, testing for biases with respect to political party, popular front runners, regions of the country, and more. Our measures suggest distinct characteristics in news and blog media. A simple generative model, in agreement with data, reveals differences in the process of coverage selection between the two media.

In this paper we propose a research agenda on the use of information markets as tools to collect, aggregate and analyze citizens’ opinions, expectations and preferences from social media in order to support public policy design and implementation. We argue that markets are institutional settings able to efficiently allocate scarce resources, aggregate and disseminate information into prices and accommodate hedging against various types of risks. We discuss various types of information markets, as well as address the participation of both human and computational agents in such markets.