94,765 research outputs found
Query Chains: Learning to Rank from Implicit Feedback
This paper presents a novel approach for using clickthrough data to learn
ranked retrieval functions for web search results. We observe that users
searching the web often perform a sequence, or chain, of queries with a similar
information need. Using query chains, we generate new types of preference
judgments from search engine logs, thus taking advantage of user intelligence
in reformulating queries. To validate our method we perform a controlled user
study comparing generated preference judgments to explicit relevance judgments.
We also implemented a real-world search engine to test our approach, using a
modified ranking SVM to learn an improved ranking function from preference
data. Our results demonstrate significant improvements in the ranking given by
the search engine. The learned rankings outperform both a static ranking
function, as well as one trained without considering query chains.Comment: 10 page
Preference evaluation techniques of preference queries in database
Preference queries are considered as a major necessity tool in today’s database management system (DBMS). Adopting preference queries in the database application systems enable users to determine more than one objective in the submitted query which result into more accurate results
compared to the traditional queries. Preference queries prefer one data item (tuple) p over the other data item (tuple) q if and only if p is better than q in all dimensions (attributes) and not worse than q in at least one dimension (attribute). Several preference evaluation techniques for preference queries have been proposed which aimed at finding the “best” results that meet the user preferences. These include but not limited to top-k, skyline, ranked skylines, k-representative dominance, k-dominance,top-k dominating, and k-frequency. This paper attempts to survey and analyze the following preference
evaluation techniques of query processing in database systems: top-k, skyline, top-k dominating, k-dominance, and k-frequency by highlighting the strengths and the weaknesses of each technique
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