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June 2008A Novel Approach for Re-Ranking of Search results using Collaborative Filtering
"... Search Engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and context. Re-ranking of the results to reflect the most relevant results to the user using the relevance feedback has received wide atten ..."
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Search Engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and context. Re-ranking of the results to reflect the most relevant results to the user using the relevance feedback has received wide attention in information retrieval in recent years. Also, sharing of information among users having similar interests using collaborative filtering techniques has achieved wide success in recommendation systems. In this paper, we propose a novel approach for re-ranking of the search results using collaborative filtering techniques using relevance feedback of a given user as well as the other users. Our approach is to learn the profiles of the users using macine learning techniques making use of past browsing histories including queries posed and documents found relevant or irrelevant. Re-ranking of the results is done using collaborative filtering techniques. First, the context of the query is inferred from the query category. The user’s community is determined dynamically in the context of the query by using the user profiles. The rank of a document is calculated using the user’s profile as well profiles of the other users in the community. 1
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"... Analyse de graphes sans échelle pour l’évaluation de similarité entre requêtes web ..."
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Analyse de graphes sans échelle pour l’évaluation de similarité entre requêtes web
Control and Cybernetics
"... Matrioshka’s soft approaches to personalized web exploration ∗ by ..."
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Title: Designing and Understanding Information Retrieval Systems using Collaborative Filtering in an Academic Library Environment Abstract approved: __________________________________________________________
"... Accessing information on the Web has become ingrained into our daily lives, and we seek information from many different sources, including conference and journal publications, personal web pages, and others. Increasingly, web-based information retrieval systems such as web-based search engines, libr ..."
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Accessing information on the Web has become ingrained into our daily lives, and we seek information from many different sources, including conference and journal publications, personal web pages, and others. Increasingly, web-based information retrieval systems such as web-based search engines, library on-line catalog systems, and subscription-based federated search systems are made available to provide an interface to collections of information from these sources. Because the quantity of new information available every day exceeds how much information individuals can handle effectively, we spend significant effort in locating information, often unsuccessfully. This dissertation consists of three scholarly articles presenting a broad set of results with the goal of helping people find interesting information in large web document collections. The results cover three specific challenges: designing and utilizing Web document recommendation systems based on human judgment, improving recommendations based on users ’ web usage as a source of implicit relevance feedback data, and understanding and designing metasearch systems for academic materials. To address these challenges, a combination of offline analysis and user studies is used.
A Study of Selection Noise in Collaborative Web Search
"... Collaborative Web search uses the past search behaviour (queries and selections) of a community of users to promote search results that are relevant to the community. The extent to which these promotions are likely to be relevant depends on how reliably past search behaviour can be captured. We cons ..."
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Collaborative Web search uses the past search behaviour (queries and selections) of a community of users to promote search results that are relevant to the community. The extent to which these promotions are likely to be relevant depends on how reliably past search behaviour can be captured. We consider this issue by analysing the results of collaborative Web search in circumstances where the behaviour of searchers is unreliable.
Under the supervision of
, 2008
"... I declare that the work described in this thesis has not been submitted for a degree at any other university and that the work is entirely my own. ..."
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I declare that the work described in this thesis has not been submitted for a degree at any other university and that the work is entirely my own.
Towards a Theory of Trust Based Collaborative Search
"... We developed three new theoretical insights into the art of hierarchical clustering in the context of web-search. A no-table example where these results may be useful is Trust Based Collaborative Search, where an active user consults agents that in the past performed a similar search. We pro-ceed wi ..."
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We developed three new theoretical insights into the art of hierarchical clustering in the context of web-search. A no-table example where these results may be useful is Trust Based Collaborative Search, where an active user consults agents that in the past performed a similar search. We pro-ceed with this as an example throughout the paper, even though the results are more broadly applicable. The
rst result is that under plausible conditions, trust converges to the extremes, creating clusters of maximal trust. The trust between any two agents, whose initial mutual trust is not maximal, eventually vanishes. In practice there is uncer-tainty about data, hence we have to approximate the
rst result with less than maximal trust. We allow clustering tolerance equal to the uncertainty at each stage. The sec-ond result is that in the context of search, under plausible assumptions, this uncertainty converges exponentially fast as we descend the clustering tree. The third observation is that Shannons cryptography may help estimate that uncer-tainty.
LNCS manuscript No. (will be inserted by the editor) Quantifying Trust
"... The date of receipt and acceptance will be inserted by the editor Abstract Trust is a central concept in public-key cryptography infrastruc-ture and in security in general. We study its initial quanti
cation and its spread patterns. There is empirical evidence that in trust-based reputation model fo ..."
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The date of receipt and acceptance will be inserted by the editor Abstract Trust is a central concept in public-key cryptography infrastruc-ture and in security in general. We study its initial quanti
cation and its spread patterns. There is empirical evidence that in trust-based reputation model for virtual communities, it pays to restrict the clusters of agents to small sets with high mutual trust. We propose and motivate a mathematical model, where this phenomenon emerges naturally. In our model, we sepa-rate trust values from their weights. We motivate this separation using real examples, and show that in this model, trust converges to the extremes, agreeing with and accentuating the observed phenomenon. Speci
cally, in our model, cliques of agents of maximal mutual trust are formed, and the trust between any two agents that do not maximally trust each other, con-verges to zero. We o¤er initial practical relaxations to the model that preserve some of the theoretical avor. Key words Trust, collaboration 1