Literature search is arguably one of the most important phases of the
academic and non-academic research. The increase in the number of published
papers each year makes manual search inefficient and furthermore insufficient.
Hence, automatized methods such as search engines have been of interest in the
last thirty years. Unfortunately, these traditional engines use keyword-based
approaches to solve the search problem, but these approaches are prone to
ambiguity and synonymy. On the other hand, bibliographic search techniques
based only on the citation information are not prone to these problems since
they do not consider textual similarity. For many particular research areas and
topics, the amount of knowledge to humankind is immense, and obtaining the
desired information is as hard as looking for a needle in a haystack.
Furthermore, sometimes, what we are looking for is a set of documents where
each one is different than the others, but at the same time, as a whole we want
them to cover all the important parts of the literature relevant to our search.
This paper targets the problem of result diversification in citation-based
bibliographic search. It surveys a set of techniques which aim to find a set of
papers with satisfactory quality and diversity. We enhance these algorithms
with a direction-awareness functionality to allow the users to reach either
old, well-cited, well-known research papers or recent, less-known ones. We also
propose a set of novel techniques for a better diversification of the results.
All the techniques considered are compared by performing a rigorous
experimentation. The results show that some of the proposed techniques are very
successful in practice while performing a search in a bibliographic database.

本篇论文旨在解决文献检索中文本相似性问题，提出了一种基于引用信息的文献检索算法，并优化了相关搜索算法以提高文献检索质量和多样性。通过实验表明，优化算法在检索文献数据库时非常成功。

引用推荐的多样化

Diversifying Citation Recommendations

Result diversification is an important aspect in web-based search, document
summarization, facility location, portfolio management and other applications.
Given a set of ranked results for a set of objects (e.g. web documents,
facilities, etc.) with a distance between any pair, the goal is to select a
subset $S$ satisfying the following three criteria: (a) the subset $S$
satisfies some constraint (e.g. bounded cardinality); (b) the subset contains
results of high "quality"; and (c) the subset contains results that are
"diverse" relative to the distance measure. The goal of result diversification
is to produce a diversified subset while maintaining high quality as much as
possible. We study a broad class of problems where the distances are a metric,
where the constraint is given by independence in a matroid, where quality is
determined by a monotone submodular function, and diversity is defined as the
sum of distances between objects in $S$. Our problem is a generalization of the
{\em max sum diversification} problem studied in \cite{GoSh09} which in turn is
a generaliztion of the {\em max sum $p$-dispersion problem} studied extensively
in location theory. It is NP-hard even with the triangle inequality. We propose
two simple and natural algorithms: a greedy algorithm for a cardinality
constraint and a local search algorithm for an arbitary matroid constraint. We
prove that both algorithms achieve constant approximation ratios.

研究了一类问题，该问题的距离是一个度量，约束是一个 matroid 中的独立性，质量则由单调子模函数确定，多样性定义为 S 中物体之间的距离之和，提出了两种算法：基于基数约束的贪心算法和基于任意 matroid 约束的局部搜索算法，并证明了两种算法都达到了恒定的逼近比。