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Prediction Approach
Solution 1
By
Harshit Srivastava
Outline
• Introduction
• Algorithms
• Algorithm 1
• Algorithm 2
• Comparison and Conclusion
Introduction
Users Choice1 Choice2 Choice3 Choice4 Choice5 Choice6 Choice7 Actions
1 J M S Q R K P
2 A C N P L K
3 M L S P...
Working of Algorithm 1
Users Choice1 Choice2 Choice3 Choice4 Choice5 Choice6 Choice7 Actions
1 J M S Q R K P
2 A C N P L K...
Algorithm One
Users Choice1 Choice2 Choice3 Choice4 Choice5 Choice6 Choice7 Actions
1 J M S Q R K P
2 A C N P L K
3 M L S ...
Result and analysis
Choices while system learns
and decision at every stage
• Stage 1 when “h” is chosen
• Order of choice...
Users Choice1 Choice2 Choice3 Choice4 Choice5 Choice6 Choice7 Actions
1 J M S Q R K P
2 A C N P L K
3 M L S P Q C I K
4 S ...
Algorithm 2
• Now, after seeing the results we can easily say that although
the system learns but prediction is still base...
• i.e., if we need to consider and think more about
nearest next neighbours as this can be useful to
predict future result...
• So, at each stage, my prediction will follow,
• 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 = α 𝑠𝑡𝑎𝑔𝑒 ∈ 𝑥1 … … … 𝑥 𝑛 + β 𝑠𝑡𝑎𝑔𝑒 ∈
𝑥1 … … … 𝑥 𝑛 + 𝛾𝑠𝑡𝑎𝑔𝑒 ∈ ...
• Now,
• For every stage
• 𝑃 − 𝑠𝑡𝑎𝑔𝑒 = (𝛼1 𝑥1 … … . 𝛼1 𝑥 𝑛)+(𝛽1 𝑥1 … … . 𝛽1 𝑥 𝑛)+(𝛾1 𝑥1 … … . 𝛾1 𝑥 𝑛)
Therefore we will 𝑃1...
𝑃1
𝑃2
𝑃3
𝑃4
𝑃5
𝑃𝑠1
𝑃𝑠2
𝑃𝑠3
𝑃𝑠4
𝑃𝑠32
𝑃𝑆12
𝑃𝑠43
𝑃𝑥
+
+
+
+
+
+
+
+ Pre stage prediction
+
𝑃𝑓𝑓
+
• Type 3
• Now, next step fu...
a b c d e f g h i j k l m n o p q r s t
P1
1 .25 2 1 .25 .25 .5
P2
.25 .25 2.25 1 .25 .25 1 .75 .5 .25 1.25 1 2.25 1.25 1
...
Conclusion
• So, after seeing the result I can easily comprehend
that history importance needs to be taken care, not
only ...
Thank You
References
Algorithm 3
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Prediction approach in predicting next user choice

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This presentation is all about, prediction approach when, n number of users have entered the system, then the n+1 th user will enter, what he will buy most favourably

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Prediction approach in predicting next user choice

  1. 1. Prediction Approach Solution 1 By Harshit Srivastava
  2. 2. Outline • Introduction • Algorithms • Algorithm 1 • Algorithm 2 • Comparison and Conclusion
  3. 3. Introduction Users Choice1 Choice2 Choice3 Choice4 Choice5 Choice6 Choice7 Actions 1 J M S Q R K P 2 A C N P L K 3 M L S P Q C I K 4 S C A B E D I 5 B N O I H G XXXXXX 6 F E B A C I P T 7 L K N P R I P T 8 Q R P C D N I 9 G H A C N T L P 10 T F E D B A 11 D B A C P S K According to question if a new user comes to search for the books what a system should refer him after initial stages of search,
  4. 4. Working of Algorithm 1 Users Choice1 Choice2 Choice3 Choice4 Choice5 Choice6 Choice7 Actions 1 J M S Q R K P 2 A C N P L K 3 M L S P Q C I K 4 S C A B E D I 5 B N O I H G XXXXXX 6 F E B A C I P T 7 L K N P R I P T 8 Q R P C D N I 9 G H A C N T L P 10 T F E D B A 11 D B A C P S K New Now, when new user comes to the system and starts browsing. My algorithm will work like this, H I G F T ?
  5. 5. Algorithm One Users Choice1 Choice2 Choice3 Choice4 Choice5 Choice6 Choice7 Actions 1 J M S Q R K P 2 A C N P L K 3 M L S P Q C I K 4 S C A B E D I 5 B N O I H G XXXXXX 6 F E B A C I P T 7 L K N P R I P T 8 Q R P C D N I 9 G H A C N T L P 10 T F E D B A 11 D B A C P S K New Now, when new user comes to the system and starts browsing. My algorithm will work like this, H I G F T ?
  6. 6. Result and analysis Choices while system learns and decision at every stage • Stage 1 when “h” is chosen • Order of choice in this stage “b,n,o,i,g” • Stage 2 when “i” is chosen • Order of choice in this stage “(t,k,g,p,l,c,f,e,b,a,n,r,m,s))” • Stage 3 when “g” is chosen • Order of choice in this stage “(t,p,k,l,c,a,n,f,e,r,m,s)” • Stage 4 when “f” is chosen • Order of choice in this stage “t,a,e,b,c,p” • Stage 5 when “t” is chosen • Order of choice in this stage “p,e,a,c,b,d,l” Decision at final stage after learning and comparing 2 stages at time • If we will notice that a,e,c is there in every stage but still p is the decision as we can observe the actions are deterministic Decision at final stage after learning and comparing all stages at time • After here if we compare all 5 stages and say, then b should be the output the decision the system showed is p. • So, I think we need to compare the history to get into real result As in algorithm 1st, the decision and prediction was totally based on actions as, whatever was the previously searched by the user the algorithm was predicting the next option in respect of previous user or items which are already bought but not in respect of particular user search history.
  7. 7. Users Choice1 Choice2 Choice3 Choice4 Choice5 Choice6 Choice7 Actions 1 J M S Q R K P 2 A C N P L K 3 M L S P Q C I K 4 S C A B E D I 5 B N O I H G XXXXXX 6 F E B A C I P T 7 L K N P R I P T 8 Q R P C D N I 9 G H A C N T L P 10 T F E D B A 11 D B A C P S K New H I G F T ?
  8. 8. Algorithm 2 • Now, after seeing the results we can easily say that although the system learns but prediction is still based on actions. • If, we look that whatever user is searching it is not based on search it is based on actions previously taken by previous users. • Then we can say algorithm 1 works in monopolistic way. • So what we can do to change the priority? • Let’s look into natural way, we say if A user went to buy apples in the market where many shops are there, he stopped by an apple shop but didn’t like it so what can be his next destination? • The chances are the shops which are near to his observability.
  9. 9. • i.e., if we need to consider and think more about nearest next neighbours as this can be useful to predict future results without disturbing actions • So, in my new algorithm I implemented a new facet with values in new priority. • Neighbours(𝛼)=1 Actions (𝛽)=0.5 Others=0.25 (whatever order they are)
  10. 10. • So, at each stage, my prediction will follow, • 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 = α 𝑠𝑡𝑎𝑔𝑒 ∈ 𝑥1 … … … 𝑥 𝑛 + β 𝑠𝑡𝑎𝑔𝑒 ∈ 𝑥1 … … … 𝑥 𝑛 + 𝛾𝑠𝑡𝑎𝑔𝑒 ∈ (𝑥1 … … … 𝑥 𝑛) • So, what I did, is if let “i” is picked the • Users who have searched “i” would be selected. • Then, the place where “i” is positioned would be assigned value 1 at n+1 and at n-1 where, n is the position of “i”. • And actions of each user stage is assigned 0.5 selected and assign 0.25 to others
  11. 11. • Now, • For every stage • 𝑃 − 𝑠𝑡𝑎𝑔𝑒 = (𝛼1 𝑥1 … … . 𝛼1 𝑥 𝑛)+(𝛽1 𝑥1 … … . 𝛽1 𝑥 𝑛)+(𝛾1 𝑥1 … … . 𝛾1 𝑥 𝑛) Therefore we will 𝑃1, 𝑃2, 𝑃3, 𝑃4, 𝑃5 from every stage after choose. • Now, we can predict next choose in three ways, Type 1 • Analyse every stage max. value and the value which is highest(like 𝑥1 𝑜𝑟 𝑥2. . 𝑜𝑟 𝑥𝑛) and most common can be seen as predicted value for next stage. Type 2 • Analyse every stage values and now just compare and add common values from previous stage like at stage 2 (𝑃𝑠2 = 𝑃1 + 𝑃2) for stage 3 (𝑃𝑠3 = 𝑃2 + 𝑃3) for stage 4 (𝑃𝑠4 = 𝑃3 + 𝑃4) and stage 5 (𝑃𝑠5 = 𝑃4 + 𝑃5) • In last at stage 5 whatever we get the last value is the predicted value for next stage.
  12. 12. 𝑃1 𝑃2 𝑃3 𝑃4 𝑃5 𝑃𝑠1 𝑃𝑠2 𝑃𝑠3 𝑃𝑠4 𝑃𝑠32 𝑃𝑆12 𝑃𝑠43 𝑃𝑥 + + + + + + + + Pre stage prediction + 𝑃𝑓𝑓 + • Type 3 • Now, next step further what I did is just sum of the stages what we got are added every time to next stage sum so in last we get total and max number of times any book is been seen and can be predicted. 𝑃𝑦 Final Stage Prediction
  13. 13. a b c d e f g h i j k l m n o p q r s t P1 1 .25 2 1 .25 .25 .5 P2 .25 .25 2.25 1 .25 .25 1 .75 .5 .25 1.25 1 2.25 1.25 1 Ps 1 1.25 .5 2.25 1 .25 .25 3 1 .75 .5 .25 1.5 1.25 2.75 1.25 1 P3 .25 .25 .25 .25 .5 .25 .5 .25 Ps 2 .5 .5 2.5 1 .25 .25 1 .75 .75 .25 1.75 1.25 2.75 1.25 P4 .75 .5 .5 .25 2 .25 1.5 Ps 3 1 .75 .75 .25 2 .25 .5 .25 .75 1.75 P5 1 .5 .5 .25 .5 .25 1.25 1.25 1.25 .25 Ps 4 1.75 1 1 .5 2.5 .25 1.25 1.25 1.5 .25 1.5 Ps 21 1.75 1 4.75 2 .5 .5 4 1 1.25 1.25 .5 3.25 2.5 5.25 1.25 1 Ps 32 1.5 1.25 1.25 1.25 2.25 .25 1 .75 1 .25 2.25 1.5 3.5 3 Ps 43 2.75 1.75 1.75 .75 4.5 .25 1.5 1.75 .25 2.25 .25 Px 3.25 2.25 6 3.25 2.75 2 2.25 .75 5.5 4 8.75 Py 4.25 3 3 2 6.75 1 2.5 .25 4 1.75 5.75 .25 PF 6 4 7.75 4.5 7.25 2.25 3.75 .75 7.25 4.25 11 1.5 Pff 4.5 2.75 6.5 2.75 5 1.75 2.75 .5 5 2.75 7.75
  14. 14. Conclusion • So, after seeing the result I can easily comprehend that history importance needs to be taken care, not only for previous user but for the current user’s next prediction. • We can observe that more the system learns the easier it becomes to predict. • We can observe that prediction was based on neighbour and actions taken by previous user but as the data gathered the algorithm showed what the user is looking for.
  15. 15. Thank You
  16. 16. References
  17. 17. Algorithm 3

This presentation is all about, prediction approach when, n number of users have entered the system, then the n+1 th user will enter, what he will buy most favourably

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