Recommender Systems Copyright: Dietmar Jannah, Markus Zanker and Gerhard Friedrich (slides based on their IJCAI talk „Tutorial: Recommender Systems”)

Recommender systems – motivation Joseph Pine: „Mass Customization” (1993) The age of standard, universal, mass products is over Various customers, various demands, heterogenous (personalised) products are needed Jeff Bezos (Amazon, CEO) „If I’ve got 2 million customer I have to have 2 million shops on the web” User adaptation! slide from: Engedy Balázs: Ajánlórendszerek

Recommender systems – motivation Problem of selection There are thousands of available products The customer has to find the best products among them (information overload)  The possible items have to be filtered/sorted! Chief objective of recommender systems: The customer sees only relevent products Personalised recommendations are needed!  The opportunity: The tracking/information harvesting about web shoping is simple It’s great for the shop keepers as it can indicate additional sales and returning cusotmers  User adaptation! slide: Engedy Balázs: Ajánlórendszerek

100K movie, 10M user, 1000M ratings

„Long tail” 30% of sales (amazon)

Recommendation task U - users, I - items, f : supervised sample from the U×I → R mapping (rating) explicit vs implicit (clicks, time spent on reading etc) ratings Machine learning task: Find f’: U×I → R which estimates f and fully defined in U×I. Recommender system:

Recommender systems - approaches Collaborative filtering: exclusively the ratings are utilised user-based: prediction is based on similar users’ preferences item-based: prediction is based on the ratings of similar items Content-based recommendation: items (content) are described by a feature set and we use only the target user’s history Hybrid methods: the combinations of the two main approaches

Evaluation Real evaluation metrics: User satisfaction! Purchase (like) of recommended products Increment in sales?

„Online” evaluation Live system, control group (Jannach, Hegelich 2009) Game app download site 150K user 6+1 groups Page visits as implicit rating 3.6% more download in the groups with recommendations No considerable difference among recommender systems

„Offline” evaluation Create train and test set from historical data, then use regression metrics: or learning-to-rank evaluation metrics

Collaborative filtering

Collaborative filtering Basic idea: Users give ratings (explicit or implicit) for items The users who behaved similarly to me will behave similarly in the future

User-based nearest neighbour recommendation Select to most similar users (peers) to the target user who rated the target item Prediction is an aggregation (e.g. average) of the ratings of the peers

User-based nearest neighbour recommendation Similarity metric? Number of peers? Aggregation method?

Similarity metric: correlation The similarity of two users(’ history) We consider only the items which are rated by both of them

Aggregation Average weighted by the similarity Normalised for the divergence of the user’s own mean (modelling of pesimistic and optimistic users)

Further problems The items might differnt weight in the similarity metric The item where everybody agrees on should have lower weight Variance of ratings per item should be incorporated into the similarity metric The similarity calculation is based on the mutally rated items. If the number of these items is small it can bias the similarities The length of the vectors should be taken into account Recognition of peers: Fix k or a threshold for the similarity values

Item-based nearest neighburhood recommendation |U| >> |I|

The „cold start” problem If a new user or a new item is introduced we don’t have any ratings. How to find similar objects? „Force” the users for rating Recursive collaborative filtering:

Dimension reduction-based recommendations

Dimension reduction-based recommendations

Collaborative filtering Intuitive Performs fine in practice No need for feature engineering It performs well with a critical mass Computational challenges because of the huge matrix… Incorporating external information is difficult

Content-based recommendation systems

Content-based recommendation systems Use exclusively the history of the target user Items are described by features e.g.: actors, director, category, words in the description Train a regression model for each of the user based on the content features

Content-based recommendation systems

Content-based recommendation systems Independent from other users (no need for critical mass) Recommendation can be given for a single user The cold start problem is smaller No need for storing/handling a huge matrix It recommends from the long tail It can give you a „user model”

Content-based recommendation systems Feature engineering is domain-specific and requires external data collection The filter bubble problem: The greatest predicted rating might be a wrong recommendation as it „overfits” to the user’s preferences E.g. if the user rated only Hungarian and Chinese restaurants the system won’t recommend a Greek restaurant (even it’s the best in the town) A new user has to be modeled, i.e. a sufficient personal training data is needed

Hybrid recommender systems Content-based → collaborative We can use content-based prediction at users with many training examples and collaboration at others The prediction of content-based models can be used in recursive collaborative filtering Collaborative → content-based Features can be extracted from other users’ ratings

Summary Collaborative filtering Content-based filtering User- and item-based nearest neighbour recommendation Content-based filtering