The goal of query conceptualization is to map instances in a query to concepts defined in a certain ontology or knowledge base. Queries usually do not observe the syntax of a written language, nor do they contain enough signals for statistical inference. However, the available context, i.e., the verbs related to the instances, the adjectives and attributes of the instances, do provide valuable clues to understand instances. In this paper, we first mine a variety of relations among terms from a large web corpus and map them to related concepts using a probabilistic knowledge base. Then, for a given query, we conceptualize terms in the query using a random walk based iterative algorithm. Finally, we examine our method on real data and compare it to representative previous methods. The experimental results show that our method achieves higher accuracy and efficiency in query conceptualization.

In this paper we present a real-world application (the first of its kind) of machine listening in the context of a live concert in a world-famous concert hall - the Concertgebouw in Amsterdam. A real-time music tracking algorithm listens to the Royal Concertgebouw Orchestra performing Richard Strauss' Alpensinfonie and follows the progress in the sheet music, i.e., continuously tracks the most likely position of the live music in the printed score. This information, in turn, is used to enrich the concert experience for members of the audience by streaming synchronised visual content (the sheet music, explanatory text and videos) onto tablet computers in the concert hall. The main focus of this paper is on the challenges involved in tracking live orchestral music, i.e., how to deal with heavily polyphonic music, how to prepare the data needed, and how to achieve the necessary robustness and precision.

Producing attractive trailers for videos needs human expertise and creativity, and hence is challenging and costly. Different from video summarization that focuses on capturing storylines or important scenes, trailer generation aims at producing trailers that are attractive so that viewers will be eager to watch the original video. In this work, we study the problem of automatic trailer generation, in which an attractive trailer is produced given a video and a piece of music. We propose a surrogate measure of video attractiveness named fixation variance, and learn a novel self-correcting point process-based attractiveness model that can effectively describe the dynamics of attractiveness of a video. Furthermore, based on the attractiveness model learned from existing training trailers, we propose an efficient graph-based trailer generation algorithm to produce a max-attractiveness trailer. Experiments demonstrate that our approach outperforms the state-of-the-art trailer generators in terms of both quality and efficiency.

Incorporating multiple types of relational information from heterogeneous networks has been proved effective in data mining. Although Wikipedia is one of the most famous heterogeneous network, previous works of semantic analysis on Wikipedia are mostly limited on single type of relations. In this paper, we aim at incorporating multiple types of relations to measure the semantic relatedness between Wikipedia entities. We propose a framework of coordinate matrix factorization to construct low-dimensional continuous representation for entities, categories and words in the same semantic space. We formulate this task as the completion of a sparse entity-entity association matrix, in which each entry quantifies the strength of relatedness between corresponding entities. We evaluate our model on the task of judging pair-wise word similarity. Experiment result shows that our model outperforms both traditional entity relatedness algorithms and other representation learning models.

In this paper, we investigate the problem of face clustering in real-world videos. In many cases, the distribution of the face data is unbalanced. In movies or TV series videos, the leading casts appear quite often and the others appear much less. However, many clustering algorithms cannot well handle such severe unbalance between the data distribution, resulting in that the large class is split apart, and the small class is merged into the large ones and thus missing. On the other hand, the data distribution proportion information may be known beforehand. For example, we can obtain such information by counting the spoken lines of the characters in the script text. Hence, we propose to make use of the proportion prior to regularize the clustering. A Hidden Conditional Random Field(HCRF) model is presented to incorporate the proportion prior. In experiments on a public data set from real-world videos, we observe improvements on clustering performance against state-of-the-art methods.

We describe an unconventional line of attack in our quest to teach machines how to rap battle by improvising hip hop lyrics on the fly, in which a novel recursive bilingual neural network, TRAAM, implicitly learns soft, context-dependent generalizations over the structural relationships between associated parts of challenge and response raps, while avoiding the exponential complexity costs that symbolic models would require. TRAAM learns feature vectors simultaneously using context from both the challenge and the response, such that challenge-response association patterns with similar structure tend to have similar vectors. Improvisation is modeled as a quasi-translation learning problem, where TRAAM is trained to improvise fluent and rhyming responses to challenge lyrics. The soft structural relationships learned by our TRAAM model are used to improve the probabilistic responses generated by our improvisational response component.

Good music recommenders should not only suggest quality recommendations, but should also allow users to discover new/niche music. User studies capture explicit feedback on recommendation quality and novelty, but can be expensive, and may have difficulty replicating realistic scenarios. Lack of effective offline evaluation methods restricts progress in music recommendation research. The challenge is finding suitable measures to score recommendation quality, and in particular avoiding popularity bias, whereby the quality is not recognised when the track is not well known. This paper presents a low cost method that leverages available social media data and shows it to be effective. Not only is it based on explicit feedback from many users, but it also overcomes the popularity bias that disadvantages new/niche music. Experiments show that its findings are consistent with those from an online study with real users. In comparisons with other offline measures, the social media score is shown to be a more reliable proxy for opinions of real users. Its impact on music recommendation is its ability to recognise recommenders that enable discovery, as well as suggest quality recommendations.

An important aspect of music perception in humans is the ability to segment streams of musical events into structural units such as motifs and phrases.A promising approach to the computational modeling of music segmentation employs the statistical and information-theoretic properties of musical data, based on the hypothesis that these properties can (at least partly) account for music segmentation in humans. Prior work has shown that in particular the information content of music events, as estimated from a generative probabilistic model of those events, is a good indicator for segment boundaries.In this paper we demonstrate that, remarkably, a substantial increase in segmentation accuracy can be obtained by not using information content estimates directly, but rather in a bootstrapping fashion. More specifically, we use information content estimates computed from a generative model of the data as a target for a feed-forward neural network that is trained to estimate the information content directly from the data. We hypothesize that the improved segmentation accuracy of this bootstrapping approach may be evidence that the generative model provides noisy estimates of the information content, which are smoothed by the feed-forward neural network, yielding more accurate information content estimates.

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user preferences. Thus, there's a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework.

Semantic relatedness (SR) measures form the algorithmic foundation of intelligent technologies in domains ranging from artificial intelligence to human-computer interaction. Although SR has been researched for decades, this work has focused on developing general SR measures rooted in graph and text mining algorithms that perform reasonably well for many different types of concepts. This paper introduces domain-specific SR, which augments general SR by identifying, capturing, and synthesizing domain-specific relationships between concepts. Using the domain of geography as a case study, we show that domain-specific SR — and even geography-specific signals alone (e.g. distance, containment) without sophisticated graph or text mining algorithms — significantly outperform the SR state-of-the-art for geographic concepts. In addition to substantially improving SR measures for geospatial technologies, an area that is rapidly increasing in importance, this work also unlocks an important new direction for SR research: SR measures that incorporate domain-specific customizations to increase accuracy.