This course homepage is accessible from https://www.cse.ust.hk/~dlee/csit6000I/

CSIT 6000I: Search Engines and Applications

 

Fall 2019

Course and Instructor/TA Information

Instructor: 

Prof. Dik Lun Lee

Email: 

dlee@cse.ust.hk

Office:

3534 (Lift 25/26)

Office Hours: 

By email appointment

 

 

TA:

Xiang Li

Email:

xiang.li@connect.ust.hk

Office:

via email

Office Hours: 

By email appointment

 

Lectures Time: 

Saturday 10:30AM - 1:20PM

Lecture Room: 

Rm 2464, Lift 25-26

 

Course Outline

Week 1

Sep 7

Introduction and course overview; Information Retrieval models: Boolean model 

Week 2

Sep 21

Information Retrieval models: vector-space model and term weighting

Week 3

Sep 28

Indexing and query processing

Week 4

Oct 5

Web-based information retrieval: Early projects, PageRank,

Week 5

Oct 12

Hub and authority webs, applications beyond search

Week 6

Oct 19

Search engine architectures: Distributed search, metasearch (mid-term 1 in last hour of lecture)

Week 7

Oct 26

Relevance feedback: Implicit and explicit feedback (mid-term 1 in last hour of lecture)

Week 8

Nov 2

Experimental Evaluation of ranking and relevance feedback algorithms

Week 9

Nov 9

Implicit feedback: query log analysis, learning to rank, Personalization

Week 10

Nov 16

Introduction to Recommendation Systems (RSs)

Week 11

Nov 23

Curiosity based and industrial projects in RSs (mid-term 2 in last hour of lecture)

Week 12

Nov 30

Summarization Systems

 

Detailed topics and slides >>> (Use your student ID as username and paasswordd)

Course Requirements

Homework

30%

3 homework assignments

 Homework 1 questions , Solution

 

 

Homework 2 questions

Homework 3 questions  Solution new!

 

 

Exercise

 

 

Mid Term

Mid Term 1 Solution

Mid Term 2 Solution new!

30%

Two mid-term exams, 1 hour each, in lecture

Higher of the two mid-term scores is taken

- Open notes, bring a calculator

Class participation

5%

0.5% for each lecture attended (5% max)

Final Exam

35%

2-2.5 hours

Bonus

5%

Participated in Q/A and discussion in lectures

 

Reference Materials

·        C.D. Manning, R. Raghavan, and H. Schutze Introduction to Information Retrieval. Cambridge University Press, 2007. Lecture slides and book are available online.

Course Description

Information retrieval techniques; traditional indexing, searching and ranking; search methods for web data, personalization, learning to rank; applications

Course Objective

At the end of the objective, the students will understand:

Policy on Academic Misconduct

Homework assignments must be done individually. Collaboration between students is strictly forbidden. Any violation will be passed to the Department's Undergraduate/Postgraduate Studies Committee for assessment. The result may lead to dismissal from the University.

 

How is bonus considered?

 

Grades are first assigned to all students without considering bonus points. Thresholds between subgrades are set. Then, bonus points are added to students. A student’s grade will be re-assigned (moved up) according to his/her new score. The end result is that students who do not have bonus points will not be penalized by other students having bonus points.

 

Warning on Open Book/Note Exams

 

Both the mid-term and final exams are open book. You can bring your lecture notes (slides and notes) and any printouts to the exam venue. You can written on the notes.  While you do not need to memorize everything (formula and pseudo code, etc.) by heart, the examinations are set assuming you know the materials well. That is, the notes/slides are there to help you with “is my cosine similarity formula correct?” and “if the PR formula 1-p… or p – 1 …” etc., but flipping through the slides page by page to find the answer of a question would waste too much time. At the end you do not have enough time to finish all of the questions. Bear in mind that you still need to study hard!