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GenDeR: A Generic Diversified Ranking Algorithm

by Jingrui He, Qiaozhu Mei, Hanghang Tong, Boleslaw K. Szymanski
"... Diversified ranking is a fundamental task in machine learning. It is broadly applicable in many real world problems, e.g., information retrieval, team assembling, product search, etc. In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary ..."
Abstract - Cited by 8 (0 self) - Add to MetaCart
Diversified ranking is a fundamental task in machine learning. It is broadly applicable in many real world problems, e.g., information retrieval, team assembling, product search, etc. In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary

Topic-Sensitive PageRank

by Taher Haveliwala , 2002
"... In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search resu ..."
Abstract - Cited by 543 (10 self) - Add to MetaCart
In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search

Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art

by David A. Van Veldhuizen, Gary B. Lamont , 2000
"... Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, ..."
Abstract - Cited by 440 (7 self) - Add to MetaCart
Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade

Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search

by Taher H. Haveliwala - IEEE Transactions on Knowledge and Data Engineering , 2003
"... Abstract—The original PageRank algorithm for improving the ranking of search-query results computes a single vector, using the link structure of the Web, to capture the relative “importance ” of Web pages, independent of any particular search query. To yield more accurate search results, we propose ..."
Abstract - Cited by 237 (2 self) - Add to MetaCart
Abstract—The original PageRank algorithm for improving the ranking of search-query results computes a single vector, using the link structure of the Web, to capture the relative “importance ” of Web pages, independent of any particular search query. To yield more accurate search results, we propose

Scalable diversified ranking on large graphs

by Rong-hua Li, Jeffrey Xu Yu - In ICDM , 2011
"... Abstract—Enhancing diversity in ranking on graphs has been identified as an important retrieval and mining task. Nevertheless, many existing diversified ranking algorithms cannot be scalable to large graphs as they have high time or space complexity. In this paper, we propose a scalable algorithm to ..."
Abstract - Cited by 13 (3 self) - Add to MetaCart
Abstract—Enhancing diversity in ranking on graphs has been identified as an important retrieval and mining task. Nevertheless, many existing diversified ranking algorithms cannot be scalable to large graphs as they have high time or space complexity. In this paper, we propose a scalable algorithm

BPR: Bayesian personalized ranking from implicit feedback

by Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme - IN: PROCEEDINGS OF THE 25TH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI , 2009
"... Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like ..."
Abstract - Cited by 153 (17 self) - Add to MetaCart
like matrix factorization (MF) or adaptive k-nearest-neighbor (kNN). Even though these methods are designed for the item prediction task of personalized ranking, none of them is directly optimized for ranking. In this paper we present a generic optimization criterion BPR-Opt for personalized ranking

Diversified Ranking on Large Graphs: An Optimization Viewpoint

by Hanghang Tong, Jingrui He, Zhen Wen, Ravi Konuru, Ching-yung Lin
"... Diversified ranking on graphs is a fundamental mining task and has a variety of high-impact applications. There are two important open questions here. The first challenge is the measure- how to quantify the goodness of a given top-k ranking list that captures both the relevance and the diversity? Th ..."
Abstract - Cited by 14 (1 self) - Add to MetaCart
Diversified ranking on graphs is a fundamental mining task and has a variety of high-impact applications. There are two important open questions here. The first challenge is the measure- how to quantify the goodness of a given top-k ranking list that captures both the relevance and the diversity

Selection of Web Services by Using Diversified Service Rank

by Ayaz Nazir Ahmed, Farooque Azam
"... Traditional techniques for web service discovery mostly rely on string matching and/or semantic logic-based matching. They are generally developed on service description and functional attributes which are exposed in service advertisement. The selection is made from the result output which is typica ..."
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agent to select the best service out of many potential candidates from the search result. This paper proposes a framework that takes into account QoS attributes along with user preferences and devise an algorithm namely Diversified Service Rank (DSR) and re-ranks top k web services in the search result

Learning to Diversify from Implicit Feedback

by Karthik Raman, Pannaga Shivaswamy, Thorsten Joachims , 2012
"... We propose an online learning model and algorithm for learning rankings that balance relevance and diversity. In each step, the algorithm presents a ranking to the user. As feedback, the algorithm observes the set of documents the user reads in the presented ranking. We propose a simple algorithm ex ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
We propose an online learning model and algorithm for learning rankings that balance relevance and diversity. In each step, the algorithm presents a ranking to the user. As feedback, the algorithm observes the set of documents the user reads in the presented ranking. We propose a simple algorithm

Diversified Social Influence Maximization

by Fangshuang Tang, Qi Liu, Hengshu Zhu, Enhong Chen, Feida Zhu
"... Abstract—For better viral marketing, there has been a lot of research on social influence maximization. However, the problem that who is influenced and how diverse the influenced population is, which is important in real-world marketing, has largely been neglected. To that end, in this paper, we pro ..."
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propose to consider the magnitude of influence and the diversity of the influenced crowd simultaneously. Specifically, we formulate it as an optimization problem, i.e., diversified social influence maximization. First, we present a general framework for this problem, under which we construct a class
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