Group recommendations

4 minute read

Updated: December 01, 2020

What if the user is a group?

Main steps for group recommendations

Obtain individual preferences – need a user profile/ model for each individual user
- Content based – need to look at the parameters and put this within the user model
- Social (collaborative) – we look for user’s interest and opinions about particular items
Aggregation strategies – to aggregate and come up with one recommendation → focus of the lec
Present the recommendations to the group with appropriate justification

Example

We have 4 users and 10 items
For each of the user, we have preferences for each item – 1 to 20
Q - How would you select the best item?
- A – with the highest score amongst many users

Take the average for each item and take the item with the highest average
If we want to recommend more than one, we take it in order
10 /20 is a nice compromise for the group
Disadvantage – the users will be disadvantaged bc strong values tend to diminish the bad choices – like 15 and 20

Choose the item with highest number of votes. → per user!!
- e.g. So u1 and u4 voted item 9 -> got 2 votes, hence chosen
Skewed - few users and few items. And also low scores are observed by the strong ones
→ item 9 !!

Considers the minimum scores for each item, and chooses the item with the maximum (minimum-scored) value (least miserable)
LM looks at the lowest item, and out of those chooses the least miserable
User will fall into disadvantage bc its with majority
! → 5 is the best choice
- User 1 is the most unhappy, but won’t be toooo bad
Can be combined with other strategies e.g. average – its easy but it can disadvantage people. However, if we do average without misery, then we take everything below 4, and item 5 will have stronger confidence now

Considers the maximum of the scores for each item, and chooses the item with the maximum value – most pleasure
So item 4 (rating = 20)

To ignore actual values and start looking at the ranking
Considers item ranking in the individual preferences, then calculates the item ranking for the group
We rank the item in order and give the value starting from 1-10. Highest gets 10 and lowest gets 1
Then we add all the items and see what the highest is

Counts how often an item beats another item (majority voting) and how many time loses – takes the difference as the value
→ We compare the item1 to every other items in both sides of the column
- e.g. Item 2 – 5,3,6,5. And we compare this to item 1 (1,3,4,9) & item 3 (3,7,15,8)
- 5 beats 1, 6 beats 4, 5 beats 3 -> so it in total, beat = 3
beats – loses = copeland. Then choose item with highest copeland value.

So far, item 5 seems the strongest as it was recommended by most of the aggregation strategies
- It won’t make people too miserable, and it gives everybody a reasonable choice assuming that user 3 isn’t the dominant user (authority)
Fairness: take the best option for everyone
Authority: take the best option for the most influential user

You recommend and see how individual members of the group feels about it.
Reading – when people was given the recommendation for the group and asked how satisfied they were, and what their friends thought
Two keys factor of people’s satisfaction with the group recommendations
- Affective state – am I happy or grumpy – people take the recommendation based on their mood and like/ dislike
- Relationships with other users -

Impact of relationships on accepting group recommendations

Several relationships that it might affect
You are not 100% satisfied, but okay with the rec
- Communal sharing -Best friend – if you have to compromise for someone you like, you’d be happy
- Authority ranking - High respect – if the recommendation was chosen by the most influential user and that person has priority, they take it even though its not their choice
Unhappy and more sensitive about not being taken into account
- Market pricing - Someone you compete with – will be fighting for fairness
- Equality matching - If you think the rec was unequal
→ When you explain the final choice, need to bear in mind the relationship between people

Obtain individual preferences
Aggregation strategies → key !!
- Most system use hybridisation of these strategies
- Evaluation is important and need to tune the strategies or heuristics → will be able to explain better
- Opinions will be influenced by affect and relationships
Presenting the recommendations to the group