COMP6714: Information Retrieval & Web Search Introduction to Information Retrieval Lecture 4: Index Construction 1 COMP6714: Information Retrieval & Web Search Plan ▪ Last lecture: ▪ Dictionary data structures ▪ Tolerant retrieval ▪ Wildcards ▪ Spell correction ▪ Soundex ▪ This time: ▪ Index construction a-hu hy-mn-z $m mace madden mo among amortize on abandon […]
COMP6714 Lecture 1 Information Retrieval and Search Engines Lecturer: Wei Wang Date: 1 Galloping Search 1.1 Description Algorithm 1: skipTo(x) 1 2 3 4 5 6 7 8 9 Data: cur is the position of the current docID under the cursor in the posting list L. L[pos] returns the docID at postion pos. /* Stage
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COMP6714: Information Retrieval & Web Search Introduction to Information Retrieval Lecture 5: Index Compression 1 COMP6714: Information Retrieval & Web Search Inverted index ▪For each term t, we must store a list of all documents that contain t. ▪ Identify each doc by a docID, a document serial number ▪Can we used fixed-size arrays for
Can you use a hashtable to implement skipTo()? Better than next() • What’stheworstcaseforsequentialmerge-based intersection? • {52, 1}èmove k2’s cursor – To the position whose id is at least 52èskipTo(52) – Essentially, asking the first i, such that K2[i] >= 52 (K2’s list is sorted). – Takes many sequential call of next() – Could use binary
COMP6714: Information Retrieval & Web Search Introduction to Information Retrieval Lecture 2: Preprocessing 1 COMP6714: Information Retrieval & Web Search Ch. 1 Recap of the previous lecture ▪ Basic inverted indexes: ▪ Structure: Dictionary and Postings ▪ Key step in construction: Sorting ▪ Boolean query processing ▪ Intersection by linear time “merging” ▪ Optimizations ▪
Name: , (Family name) (Given name) Student ID: THE UNIVERSITY OF NEW SOUTH WALES Final Exam COMP6714 Information Retrieval and Web Search SESSION 2, 2011 • Time allowed: 10 minutes reading time + 3 hours • Total number of questions: 10+1 • Total number of marks: 100+5 • Only UNSW approved calculators are allowed in
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EECS 485 Final Exam Spring 2021 SOLUTION This is a 120 minute, open-note exam. Allowed Resources ● You may use any notes or other resources, including online resources. Copyright By PowCoder代写 加微信 powcoder ● You may use a compiler, IDE, or other programming tools. Collaboration/Assistance is Prohibited ● You are NOT allowed to collaborate with
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Lecture 17-18 – Big Data Morandini Cloud Architect – Melbourne eResearch Group University of Melbourne Outline of the Lecture ● Part 1: Introduction to big data analytics Copyright By PowCoder代写 加微信 powcoder ○ Types of analysis performed ○ Distributed computing on big data ● Part 2: Apache Hadoop ○ The Hadoop ecosystem ○ Hadoop Distributed
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Recommender Systems COMPSCI 753 Kaiqi Zhao Recommendation algorithms § Content-based § Recommend based on the attributes of items, e.g., functionality, topics, types, etc. § Collaborative Filtering (CF) § User-based: recommend items that similar users liked § Item-based: recommend similar items to the items the user liked 10 Content-based recommendation § Content-based methods look for the
程序代写代做代考 information retrieval algorithm Recommender Systems Read More »
Approximate Near Neighbor Search: Locality-sensitive Hashing COMPCSI 753: Algorithms for Massive Data Ninh Pham University of Auckland Auckland, Aug 10, 2020 1 Outline Popular similarity/distance measure Definitions Applications Locality-sensitive hashing framework for approximate near neighbor search LSH definition LSH framework for near neighbor search 2 A common metaphor