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Search result diversification has gained attention as a way to tackle the ambiguous or multi-faceted information needs of users. Most existing methods on this problem utilize a heuristic predefined ranking function, where limited features can be incorporated and extensive tuning is required for different settings. In this paper, we address search result(More)
Topic-focused multi-document summarization aims to produce a summary over a set of documents and conveys the most important aspects of a given topic. Most existing extractive methods view the task as a multi-criteria ranking problem over sentences, where relevance, salience and diversity are three typical requirements. However, diversity is a challenging(More)
Although marketing researchers and sociologists have recognized the large impact of life stage on consumer's purchasing behaviors, existing recommender systems have not taken this impact into consideration. In this paper, we found obvious correlation between life stage and purchasing behavior in many E-commerce categories. For example, a mum may look for(More)
Proximity among query terms has been recognized to be useful for boosting retrieval performance. However, how to model proximity effectively and efficiently remains a challenging research problem. In this paper, we propose a novel proximity statistic, namely Phrase Frequency , to model term proximity systematically. Then we propose a new proximity-enhanced(More)
An efficient aminocatalytic enantioselective Michael addition of readily available cyclic hemiacetals to nitroolefins has been developed. The strategy serves as a powerful approach to synthetically valuable chiral 3-substituted tetrahydrofurans (THFs) and tetrahydropyrans (THPs). The synthetic utilities of the versatile Michael adducts also have been(More)
ListMLE is a state-of-the-art listwise learning-to-rank algorithm, which has been shown to work very well in application. It defines the probability distribution based on Plackett-Luce Model in a top-down style to take into account the position information. However, both empirical contradiction and theoretical results indicate that ListM-LE cannot well(More)
With the quick development of online social media such as twitter or sina weibo in china, many users usually track hot topics to satisfy their desired information need. For a hot topic, new opinions or ideas will be continuously produced in the form of online data stream. In this scenario, how to effectively filter and display information for a certain(More)
Relevance and diversity are both crucial criteria for an effective search system. In this paper, we propose a unified learning framework for simultaneously optimizing both relevance and diversity. Specifically, the problem is formalized as a structural learning framework optimizing Diversity-Correlated Evaluation Measures (DCEM), such as ERR-IA, α-NDCG and(More)
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