Reverse Nearest Neighbor Heat Maps: A Tool for Influence Exploration

We study the problem of constructing a reverse nearest neighbor (RNN) heat map by finding the RNN set of every point in a two-dimensional space. Based on the RNN set of a point, we obtain a quantitative influence (i.e., heat) for the point. The heat map provides a global view on the influence distribution in the space, and hence supports exploratory analyses in many applications such as marketing and resource management. To construct such a heat map, we first reduce it to a problem called Region Coloring (RC), which divides the space into disjoint regions within which all the points have the same RNN set. We then propose a novel algorithm named CREST that efficiently solves the RC problem by labeling each region with the heat value of its containing points. In CREST, we propose innovative techniques to avoid processing expensive RNN queries and greatly reduce the number of region labeling operations. We perform detailed analyses on the complexity of CREST and lower bounds of the RC problem, and prove that CREST is asymptotically optimal in the worst case. Extensive experiments with both real and synthetic data sets demonstrate that CREST outperforms alternative algorithms by several orders of magnitude.Comment: Accepted to appear in ICDE 201

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Using Search Engine Technology to Improve Library Catalogs

This chapter outlines how search engine technology can be used in online public access library catalogs (OPACs) to help improve users’ experiences, to identify users’ intentions, and to indicate how it can be applied in the library context, along with how sophisticated ranking criteria can be applied to the online library catalog. A review of the literature and current OPAC developments form the basis of recommendations on how to improve OPACs. Findings were that the major shortcomings of current OPACs are that they are not sufficiently user-centered and that their results presentations lack sophistication. Further, these shortcomings are not addressed in current 2.0 developments. It is argued that OPAC development should be made search-centered before additional features are applied. While the recommendations on ranking functionality and the use of user intentions are only conceptual and not yet applied to a library catalogue, practitioners will find recommendations for developing better OPACs in this chapter. In short, readers will find a systematic view on how the search engines’ strengths can be applied to improving libraries’ online catalogs

Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness