The document describes a group recommendation system that provides recommendations based on the opinions of groups of users. It analyzes subgroups within a larger group that share the same rating for an item, and recommends the item rating that the largest subgroup agrees on. This helps reduce information overload by focusing recommendations on items with a consensus rating. The system forms groups based on similar ratings using a relationship matrix. It then applies a DeGroot model to modify ratings based on neighboring opinions and identify the highest predicted rating to recommend.

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Opinion Dynamics-Based GroupRecommender Systems
Now-a-days lots of data gather on internet which help user to get his desire data
(example selecting any service or product online) by making search with
interested query but some time query result will fetch huge amount of response
(also known as information overload) which make user uncomfortable in getting
desire data. To overcome from this issue lots of techniques are introduce such as
Personalize Recommendation System which allow user to get search results
from his past search history, Group Recommendation System in which relevant
products search by many users in same group will be recommended to new
users.
Above existing techniques reduce size of search data but this technique will not
use relationships by considering opinions of all peoples in same group for single
service/product, in same group for same product some user give high rating and
some may give low rating and this is called as (agree, not agree or consensus).
If maximum peoples give same rating then this peoples are consensus or agree
with given rating. In this paper author is using same relationship techniques to
recommend items based on Group Based Opinion Recommendation System.
In a group different peoples may give different ratings for same product and to
give correct recommendation we need to extract sub opinions (different rating)
of all peoples and then calculate that rating which is maximum peoples give and
this maximum peoples (all this peoples giving same rating for same item, so that
are in same group with relationship) agree for same rating can be best
recommendation for the new users instead of calculating all ratings without
considering any relationship. Here we are giving recommendation based on
relationships so response size will also be reduce and user can easily select
desired data from less response.
Group Based Recommendation System with Opinion consists of following
modules.
1) Compute individual predictions: In this module we will form a Singular
Matrix Decomposition (SVD) with all individual users giving ratings to
many items.
2) Compute Relationships Between Members Preferences: In this module
we will build relationship matrix by looking for all users who gave
similar rating to same items. If all users think product is good then they
will give similar rating and based on this rating we will form a group with

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relationship. Finding similar users from aa group with relationship also
called as PRE-GROD.
3) Predict Group Rating Using DeGroot’s Model: DeGroot’s Model is based
on person name which says that in online social networks people changed
their opinions based on their neighbour’s opinion. Here also we can look
for change in rating by applying this model. By applying this model we
will look for similar rating score which maximum peoples give for same
items and this score is also called as MAX score. Once we get MAX
score then we will check existing group score in matrix and if there is a
variation between existing score and MAX Score then we will modify
similarity matrix with new MAX score. This MAX score also called as
High Prediction Score and can be recommended to new users. Applying
this technique also called as GROD.
4) Recommend Highest Prediction Items: Using this module we will display
all calculated MAX Score as Highest Prediction.
Example
User Item Rating
U1 I1 3
U2 I1 3
U3 I1 5
U4 I1 2
U5 I1 3
In above example all users gave rating to same item I1. When we sum up all
ratings then average rating = 16/5 = 3.2. but in above table 3 users are giving
same rating which means maximum users are agree and other two users are not
agree. So we will take ratings of only those users who are agree and recommend
to new users with highest rating with 3 agree users
MAX Score OR Highest rating = (3 * 3)/3 = 9;
Similar rating users = 3
Similar rating = 3 (from all 3 users got same rating)
Number of similar rating users = 3;
To implement this project author used MOVIE LENS dataset and I am also
using same dataset to get this project output. Below are some records from
dataset.
user id item id rating timestamp
196 242 3 881250949

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186 302 3 891717742
22 377 1 878887116
Here all records are anonymised with integer values to provide security to user
data. In above dataset 196 is user id, giving rating to item 242 and 3 is the rating
and last value is the time
Screen shots
Double click on ‘run.bat’ file to get below screen
In above screen click on ‘Upload Movie Lens Dataset’ button and upload
dataset

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After dataset upload will get below screen
In uploaded dataset 10000 records are there. Now click on ‘Compute Individual
Predictions’ button to calculate rating of each user for all items

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In above screen we can see total 943 users are there which are giving rating to
items. Now click on ‘Compute Relationships Preferences (Pre-GROD)’ button
to find all users who are giving rating to similar item and forms a group
In above screen In selected record first value 242 is the item ID and 3 is the
rating for that item and remaining numbers are the user ID’s giving rating to this
item. Similarly we can see rating and group users for all items. In above screen

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according to PRE-GROD 3 is the rating for 242 item ID but when we apply
DeGroot’s model then rating will change which will consider as true and
Highest rating and this new rating will modify inside matrix. Now click on
‘Group Rating Applying DeGroot’s Model (GROD)’ button to calculate highest
rating and to modify matrix
In above screen we can see similarity matrix modified by applying GROD
model, now click ‘Recommend Items with Highest Prediction’ button to see the
modified value

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In above screen we can see GROD modified new value is 4 for item ID 242 and
its previous score was 3. Above rating calculated by analysing sub opinions
instead of analysing all opinions. Now click on ‘Mean Absolute Graph(MAE)’
button to get MAE graph.
In above screen MAE graph (MAE means error between true output and
prediction by using application). In above graph x-axis represents technique
name and y-axis represents error value. In above graph Baseline technique not
using GROD model so its prediction error is high compare to GROD model.
GROD model prediction error is reduce as its using sub opinions only to get
correct prediction.