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Lecture slides and references for CS290N (Tentative)

Slides and lecture orders may be adjusted and this page is updated periodically based on the pace of class presentation, project needs, and the feedback from you.

Weeks 1 and 2

• 290N Introduction
• Introduction to Search Engines.
• Information retrieval model.
• Open search engines and Lucene/Solr .
• Indexing.
• Project ideas

References:

• T. Yang, A. Gerasoulis, Web Search Engines: Practice and Experience . To appear in Computer Science Handbook (T. Gonzalez. Eds), Chapman & Hall/CRC Press.
• Retrieval models, index construction/compression of textbook [MRS]
• Apache Lucene tutorial . Solr tutorial . Scaling Lucene/Solr
• Inverted files for text search engines by Justin Zobel, Alistair Moffat, ACM Computing Survey, Vol 38, No.2, 2006

Weeks 3 and 4

• Query Processing Architecture and Online Systems
• Ranking and Learning

References:

• Computing scores , Learning to rank, and Text classification in [MRS]
• Neptune: Scalable Replica Management and Programming Support for Cluster-based Network Services. by K. Shen, et. al, 3rd USENIX Sympo. on Internet Technologies and Systems (USITS '01).

Weeks 5 and 6

• Classification Algorithms. Learning ensembles.
• Duplicate Detection and Similarity Computing
• Parallelism in Search and Data Processing: MapReduce with Hadoop. Facility.

References:

• Machine learning in automated text categorization by Fabrizio Sebastiani, ACM Computing Surveys, Vol 34 2002.
• Near duplicates from [MRS]
• MapReduce.
• Parallel Boosted Regression Trees for Web Search Ranking

Weeks 7 and 8

• Search engine evaluations.
• Crawler in Search Engines.
• Offline data processing.
• Click Analysis.
• Research paper reviewing/project progress.

References:

• Evaluation
• Improving web search ranking by incorporating user behavior information by Eugene Agichtein, Eric Brill, Susan Dumais. SIGIR 2006.
• Crawling of textbook [MRS].

Week 9-10

• Other topics:
• SVM , Bigtable etc. based on class interests.
• Recommendation.
• Computational advertisement
• Class Project/Technical Paper Presentation

References:

• Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. G. Adomavicius , A. Tuzhilin, IEEE Transactions on Knowledge and Data Engineering. 2005
• SVM of textbook [MRS]
• Large-scale Incremental Processing Using Distributed Transactions and Notifications, D. Peng, F. Dabek, OSDI 2010.