Muhammad Sarwar, Mahamudul Hassan, Masum
Billal, Dmitry Ignatov, “Similarity
Aggregation for Collaborative Filtering”, AIST
2015, Yekaterinburg, Russia.
Falguni Roy, Sheikh
Hassan, “User Similarity Computation for Collaborative Filtering
Using Dynamic Implicit Trust”, AIST 2015, Yekaterinburg,
Khaleda Akhter, Sheikh Muhammad Sarwar, “Analysis of the
Adaptive Nature of Collaborative Filtering Techniques In Dynamic Environment”, AIST 2015, Yekaterinburg, Russia.
Noman Bin Mannan, Sheikh
Muhammad Sarwar, Najeeb Elahi, “A New User Similarity Computation Method for
Collaborative Filtering Using Artificial Neural Network”, EANN
2014, Sofia, Bulgaria.
Hafiz Md. Hasan Babu, Nazir
Saleheen, Lafifa Jamal, Sheikh Muhammad Sarwar, Tsutomu Sasao, “Approach to design a compact reversible low
power binary comparator”, IET Computers and Digital
Techniques, Volume 8, Issue 3, May 2014.
Muhammad Sarwar, Md. Mustafizur
Rahman, Md. Haider Ali,
Ashique Mahmood Adnan,
“A Scalable Image Snippet Extraction
Framework for Integration with Search Engines”, Computer and
Information Science 6(1): 89-99 (2013)
ShafaetAshraf,Sheikh Muhammad Sarwar, Md. Abeed Hassan, Dr. Saifuddin Md. Tareeq,
Anna Fariha, “An
Efficient Method for Extracting Subtrees against Forest Query”,
IMCOM 2015, Bali, Indonesia.
Sheikh Muhammad Sarwar, Md.
Anowarul Abedin, Abdullah-Al-Mamun, “Personalized Query
Expansion for Web Search Using Social Keywords”,
iiWAS 2013, Vienna, Austria.
Muhammad Sarwar, Ishtiaque Hossain, “A Novel Reduced Reference Image Quality Analysis
Metric for JPEG Compressed Images Based on Image Segmentation”,
ICIEV, 2013, Bangladesh.
Sheikh Muhammad Sarwar
Mosaddek Hossain Kamal, "Integration of Novel Image Based Features into Markov Random Field Model
for Information Retrieval
MAW, 2012, Fukuoka, Japan.
CSE 804: Information Retrieval
Objective: The aim of this course is to give
an understanding about the basic architechture of a search engine. Students
will learn about efficient data structures for storing and indexing
unstructured and semi-structured documents. They will also learn about the
algorithms for ranking and scoring retrieved documents. However, crucial tasks
like online and offline evaluation of search engines will also constitute a
major part of the course.
Outline: Boolean Retrieval: Inverted Index,
Processing boolean queries, extended Boolean retrieval; Term Vocabulary and
Postings lists: Document delineation and character sequence decoding,
Tokenization, Dropping common terms: stop words, Normalization (equivalence
classing of terms), Stemming and lemmatization, skip pointers, Biword indexes,
Positional indexes; Dictionaries and tolerant retrieval: Search structures for
dictionaries, General wildcard queries, k-gram indexes for wildcard queries,
Spelling correction; Index Construction: Blocked sort-based indexing,
Single-pass in-memory indexing, Distributed indexing, Dynamic indexing; Scoring
and Ranking: Parametric and zone indexes, Term frequency and weighting, The
vector space model for scoring, variant tf-idf functions; Computing scores in a
complete search system: Efficient scoring and ranking, Components of an
information retrieval system; Evaluation in information retrieval: Evaluation
of unranked retrieval sets, Evaluation of ranked retrieval results, Assessing
relevance, Results snippets; Relevance feedback and query expansion: The
Rocchio algorithm for relevance feedback, Relevance feedback on the web,
Evaluation of relevance feedback strategies, Global methods for query
reformulation; Language models for information retrieval; Enterprise
Information Retrieval: Explore the capacity of Apache Lucene as a text search