The document summarizes a study on rating algorithms. It discusses 3 algorithms - FairJudge, an iterative ranking algorithm with reputation redistribution, and the Elo rating algorithm. It then proposes merging the FairJudge and Elo algorithms by using FairJudge to identify fair/unfair users, comparing product reliabilities with Elo, and sorting the results. The study initializes values, outlines the integration model and steps, and provides initial output showing average ratings and the first prototype of the merged FairJudge/Elo algorithm coded in Python.

1. A study on Rating Algorithms
Sikkim University
Department of Computer Application
School of Physical Sciences
Presented by:
Robin Gurung & Ashish Pradhan
Roll no:14UCA015 & 14UCA005

2. Contents
1. Abstract
2. Introduction
3. Literature Review
3.1 Brief Study of Filtered Research paper
3.2 Paper Studied in detail.
4. Contribution to the paper study.
4.1 Rating algorithm concept.
4.2 Model integration of algorithm.
5. Outcomes

3. ABSTRACT
• Studied on different kind of rating system in the
market
• 3 algorithm has been discussed.
1) FairJudge algorithm
2) Iterative ranking algorithm with reputation
redistribution.
3) Elo algorithm.
• Merged 2 algorithms for a new and better output.
• Helps the market to compare the product.

4. INTRODUCTION
• Rating system been used over all the online
market platform in online world.
• A way of recognizing good product.
• Generally people go for mean Method.
• Unreasonable rating (Spammers, lack of
knowledge).

5. LITREATURE REVIEW
Trustworthy User Prediction in Rating Platforms”
Feasibility study.
• FairJudge algorithm
• The paper propose three metrics:
1. The fairness of a user
2. The goodness of a product.
3. The reliability of a rating
•The FairJudge algorithm is already being deployed
at Flipkart.

6. LITREATURE REVIEW
Hao Liao, An Zeng, Rui Xiao “Ranking Reputation
and Quality in Online Rating Systems”
• A Reputation redistribution process is introduced.
• Effectively enhance the weight of the highly
reputed users.
•Tested on both Artificial and real data.
• two penalty factors to the iterative ranking
algorithm.

7. LITREATURE REVIEW
Algorithm for Ranking Consumer Reviews on
Ecommerce Websites”
• content analysis from a grammatical, sentimental
and relevance point of view.
• Outcome is fed into the neural network for weight
of users.

8. LITREATURE REVIEW
University of Edinburgh by Marius St˘anescu,
“Rating systems with multiple factors”
•Insight on the strategy of the gamer.
•One can get a very high percentage just by choosing
weak players, easily defeated.
• Another problem is that skills change in time

9. LITREATURE REVIEW
University of Edinburgh by Marius St˘anescu,
“Rating systems with multiple factors”
•Insight on the strategy of the gamer.
•One can get a very high percentage just by choosing
weak players, easily defeated.
• Another problem is that skills change in time

10. Brief Study of filtered
research Paper
• Iterative ranking Algorithm
• FairJudge Algorithm
• Elo rating Algorithm

11. 1) Iterative ranking algorithm with
reputation redistribution
•Process for eliminating noisy information in the
iterations,
•so as to improve the accuracy in objects’ quality
ranking.
•The users are denoted by set U and objects (e.g.
books, movies or others) are denoted by set O.
•The rating given by a user i to object a is the weight
of the link, denoted by riα

12. • The degree of users and objects are respectively
ki and ka.
we define the set of objects selected by user i as
Oi and the set of users selecting object a as Ua.

13. • Three novel metrics to quantify the
trustworthiness of users and the quality of
products, building on our prior work.
• Fairness, Goodness and Reliability.
• Fairness : Fair users rate products without bias,
i.e. they give high scores to high quality
products, and low scores to bad products.
2) FairJudge algorithm

14. • The fairness F(u) of a user u lies in the [0,1]
interval ∀ u ∈U.
• 0 denotes a 100% untrustworthy user, while
1 denotes a 100% trustworthy user.
Goodness : The quality of a product determines
how it would be rated by a fair user
• The goodness G(p) of a product p ranges
from −1 (a very low quality product) to +1 (a
very high quality product) ∀p ∈P.

15. • Reliability : This measure reflects how
trustworthy the specific rating is.
• The reliability R(u,p) of a rating (u,p) ranges
from 0 (an untrustworthy rating) to 1 (a
trustworthy rating) ∀(u,p) ∈R .
Fig 1: Reliablitly of users in Flipcart

16. Figure 2: Toy example showing products (P1, P2, P3),

17. 3) Elo rating Algorithm
• The Elo rating system is a method for calculating
the relative skill levels of players in zero-sum
games such as chess.
• Players with higher ELO rating have a higher
probability of winning a game than a player with
lower ELO rating.
• After each game, ELO rating of players is updated.

18. • If a player with higher ELO rating wins, only a few
points are transferred from the lower rated
player.
• However if lower rated player wins, then
transferred points from a higher rated player are
far greater.

19. • CASE-1: Suppose Player 1 wins: rating1 = rating1 +
k*(actual – expected) = 1200+30(1 – 0.76) =
1207.2rating1
• rating2 = rating2 + k*(actual – expected) =
1000+30(0 – 0.24) = 992.8;
• Case-2: Suppose Player 2 wins: rating1 = rating1 +
k*(actual – expected) = 1200+30(0 –
• 0.76) = 1177.2; rating2 = rating2 + k*(actual –
expected) = 1000+30(1 – 0.24) = 1022.8;

20. Paper Studied in Detail
• FairJudge Algorithm
• Elo rating Algorithm

21. Cold Start Problem
• Initially very low information about the user.
• Cant trust their given ratings to product.
• For products that have only been rated a few times,
it is hard to accurately determine their true quality.
• This uncertainty due to insufficient information of
less active users and products is the cold start
problem.

22. Fairness of user
Goodness of product

23. Incorporating Behavioural
Properties
• Rating scores alone are not sufficient to efficiently
estimate the fairness, goodness and reliability
values.
• The behavior of the users and products is also an
important aspect to be considered.
• Fraudsters have been known to give several ratings
in a very short timespan.

24. • Beginning users, on the other hand, have a more
spread out rating behavior as they lack regularity.
• A user u’s temporal rating behavior is represented
as the time difference between its consecutive
ratings, inter-rating time distribution IRTDU(u).
• BIRDNEST algorithm, which calculates a Bayesian
estimate of how much user u’s IRTDU(u) deviates
from the global population of all users’ behavior.

25. BIRDNEST ALGORITHM
• Analyze the behavior of
the user w.r.t time.
Factors involved:
Frequency , score,
reliablility.
NEST: higher the value of
nest more unfair a user is.
Fig 3: Birdnest algorithm

26. Contribution to the Paper
Study

27. Rating Algorithm Concept
• Users give rating to a product.
• Calculate fairness of rating by user.
• calculate Reliability comparing with another
same domain product.
• Outcome: filtered ranking of the products of
same domain

28. Model Integration of Algorithm
Used Fair Judgment and Elo rating algorithm.
• Initially average method used for rating of
product.
• Fair Judgment is used to verify fair and unfair
users.
• Compare the reliability using GLICKO/Elo Rating.
• Sort the ranking .

29. Fair Judgment Algorithm
* COLDSTART PROBLEM
*BIRDNEST ALGORITHM
OUTCOME: Fair/Unfair
Rating.
Rating
Product
Comparing Reliability
using
GLICKO/Elo Rating
Sorting Results
Fair Judgment Algorithm
* COLDSTART PROBLEM
*BIRDNEST ALGORITHM
OUTCOME: Fair/Unfair
Rating.
Rating
Product
Fig 4: Integration Model of
Algorithm

30. Initialization Concept
 alpha1, alpha2;
beta1, beta2; (ranges from 0-1).
IBD(Birdnest 1 to 5).
error = 0.000001
 In|p|= Total no. of rates received
by the product.
E.g. 6 users gives rating to
product P1. (Here, In|p|=6)
 Out|u| = Total no. of products
rated by user U1.
E.g. U1 rated 3 prod
(Here, Out|u|= 3)
• Value of alpha1,2; beta1,2 and
IBD shall be changed to see the
different results from the
Algorithm.
• Output: the results will be
compared and if the highest is
greater than the error rate than
the loop will continue to find
optimal solution.
FairJudge Algorithm
Initialization part

31. STEPS
Step 1: int t = 0
Step 2.1: score board, S1[][] (user Ui and
product Pj)
Step 2.2: F1[] (for products Pi ) = 1.
Step 2.3: RI[][] (same as Step 2). = 1.
Step 2.4: G1[] ( of Pi )
Step 3: Start loop (do while)
Step 3.1: t=t+1
3.2: G2[x]=K1+[(S1[i][j]*R1[i][j] )+
( S2[i++][j]*R1[i++][j] )/ K2 ……
3.3: G2[x++] K1+(S1[i][j++]*R[i][j++])
Assumptions
• [] =1D Array.
• [][] = 2D Array.
• F1 = Fairness, R1 = Reliability, G1 =
Goodness.
• Int i,j,t,x=0
• K1= beta2 * IBD
• K2 = beta1 + beta2 + IBD
S1 R1 S1 R1 S1 R1
0 0 0 1 0 2
1 0 1 1 1 2
2 0 2 1 2 2

32. Step 4: R2[i][j] = F1[i]+[i-[c]/2]
4.1: R2[i][j++] = “”
Step 5: F2[i] = k1 + R[i][j] + R[i][j++] +
R[i][j++]/k2
Step 6: G3[] = G1[]-G2[]
R3[][] = R1[][]-R2[][]
F3[] = F1[]-F2[]
Step 7: int x = highest(G3[])
Step 7.1: int y = highest(R3[][])
Step 7.2: int z = highest(F3[])
Step 8: array x[] ={x,y,z}
Step 8.1: highest = sort_highest(x)
• i=0,j=0;
• C= S1[j][i] - G2[i]
• C = s1[j][i++] - G2[i++]
if (j.equlas (length of array))
{ i=0,j++; }
• G3[], R3[][], F3[] new arrays.
Step 8.2: if ( highest > error )
Step 8.3: Repeat loop
Step 8.4: stop the program
AssumptionsSTEPS

33. Outcome
• Initial Rating for a
Product From various
users.
• Calculating the
average value of the
rating.
Fig 5: initial rating (average)

34. Outcome

35. Fig 6: First prototype of FDA
(Output)

36. Fig 6.1: First prototype of FDA
(Output)

37. Fig 7: First prototype of FDA (Coded in Python)

38. Fig 7.1: First prototype of FDA (Coded in Python)

39. THANKYOU