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434
GenDeR: A Generic Diversified Ranking Algorithm
"... 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
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Cited by 8 (0 self)
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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
, 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
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Cited by 543 (10 self)
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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
, 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
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Cited by 440 (7 self)
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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
- 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
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Cited by 237 (2 self)
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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
- 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 ..."
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Cited by 13 (3 self)
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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
- 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
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Cited by 153 (17 self)
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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
"... 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
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Cited by 14 (1 self)
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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
"... 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
, 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 ..."
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Cited by 3 (0 self)
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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
"... 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
Results 1 - 10
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434