1. Agent Technology for e-Commerce
Chapter 6: Recommender Systems
Maria Fasli
Maria Fasli, University of Essex

2. Recommender systems: The problem
Too much information: information overload – consumers have too many options
A recommender system is a system which provides recommendations to a user
Applications: Books, music CDs, movies. Even documents, services and other products such as software games

3. Recommender Provider n  Provider 1 Recommender Request for service
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of capabilities
Sorted description
of P1,..Pn
Service delegation
Results of service request
Requester

4. Information needed
Information used for recommendations can come from different sources:
browsing and searching data
purchase data
feedback explicitly provided by the users
textual comments
expert recommendations
demographic data

5. Providing recommendations
Recommendations can take the following forms:
Attribute-based recommendations: based on syntactic attributes of products (e.g. science fiction books)
Item-to-item correlation (as in shopping basket recommendations)
User-to-user correlation (finding users with similar tastes)
Non-personalized recommendations (as in traditional stores, i.e. dish of the day, generic book recommendations etc.)

6. Recommendation technologies
Information retrieval (IR) systems:
allow users to express queries to retrieve information relevant to a topic of interest or fulfil an information need
they are not useful in the actual recommendation process
they cannot capture any information about the users’ preferences
they cannot retrieve documents based on opinions or quality as they are text-based
To address these issues two techniques have been developed:
Content-based filtering (Information filtering)
Collaborative-based filtering

7. Content-based filtering
The system processes information from various sources and tries to extract useful elements about its content
keyword-based search (keywords sometimes in boolean form)
semantic-information extraction by using associative networks of keywords, or directed graphs of words

8. Each user is assumed to act independently and the system requires a profile of the user’s needs or preferences
The user has to provide information on her personal interests on starting to use the system for the profile to be built
The profile includes information about the items of interest, i.e. movies, books, CDs etc.
Content-based filtering techniques try to identify similar items which are returned as recommendations
They do not depend on having other users in the system

9. Issues
Pure content-based filtering systems are not capable of exploring new items and topics
Over-specialization: one is restricted in viewing similar items
Difficult to apply in situations where the desirability of an item is determined in part by aesthetic qualities that are difficult to quantity – it is difficult to apply content-based analysis to such items

10. The user profiles
For the system to produce accurate recommendations, the user has to provide constant feedback on the returned suggestions – users do not like providing feedback
Consist entirely of ratings of items and topics of interest: the fewer the ratings, the more limited the set of possible recommendations
As the user’s interests change, these changes need to be tracked

11. Collaborative filtering
Collaborative-based filtering systems can produce recommendations by computing the similarity between a user’s preferences and the preferences of other people
Such systems do not attempt to analyse or understand the content of the items being recommended
They are able to suggest new items to user who have similar preferences with others

12. Basic mechanism A large group of people’s preferences are registered
A subgroup of people is located whose preferences are similar of the user who seeks the recommendation
An average of the preferences for that group is calculated
The resulting preference function is used to recommend options to the user who seeks the recommendation
The concept of similarity needs to be defined in some way

13. Example user-item matrix
What would be the recommendation for user D?

14. Neighbourhood-based algorithms
Three steps
(i) The degree of similarity of the active user and the others in the database is calculated (positive or negative)
(ii) A set of users is chosen as the basis for making the prediction. This is determined based on the degree of similarity and differs from system to system
(iii) The set of users chosen in the previous step is used to make the recommendation. A user with high degree of similarity may be assigned higher weight

15. Pearson’s correlation coefficients
It reflects the degree of linear relationship between two variables and ranges –1 to +1
The degree of correlation between an active user a and another user u is:

16. Next, the neighbourhood of users based on which the recommendation will be provided is selected
The weighted average of the ratings of the neighbourhood of users for the item of interest is then calculated as follows:

17. Example Using Pearson’s correlation coefficients:
wD,A= wD,B= wD,C= 0
pD,Item4= 4.5

18. Issues
A critical mass of users is needed in order to create a database of preferences: first-rater or cold start problem
New items cannot be recommended until someone has rated them
The scarcity of ratings (the user profiles are sparse vectors of ratings) also presents a problem
Recommendations will come from users with which the active user shares ratings (or votes) – this presents a problem to methods such as Pearson’s correlation coefficients; potential solutions: default voting

19. Scalability: in systems with a large number of items and users, computation grows linearly; appropriate algorithms that scale up are needed
Reliability, especially in reputation systems: content providers inflate their ratings
Lack of transparency: the user is given no indication whether to trust a recommendation – incorporating explanation systems would help address this concern
Privacy – once a system has built your profile, who else can have access to it?

20. Combing collaborative and content-based filtering
The underlying idea is that the content is also taken into account when attempting to identify similar users for collaborative recommendations
A number of systems have been developed: Fab, Tango, the Recommender system, GroupLens’ approach

21. Recommender systems in e-commerce
Turning browsers into customers: they can stimulate the users’ needs (need identification stage)
Cross-selling: suggest additional products which may match the user’s interests or current shopping basket
Personalization: personalized services, or the site can be personalized to the user’s liking – unique shopping experience
Keeping customers informed
Retaining customer loyalty

22. Personalization
Vendors can identify exactly who is visiting their store through registration, cookies, spyware
Vendors can personalize their websites for their customers
They can keep track of preferences, actions, they can build profiles of their users. These can be used for marketing
Vendors can measure the users’ desires – dynamic pricing
When the consumer is unaware, then problems arise, possible breaches of the user’s privacy. Who else gains access to these profiles?
Negative impact on consumer confidence