RECOMMENDER SYSTEMS

Presented By:Madiha SaleemSunniya Rizvi

Collaborative Filtering Collaborative filtering is a technique used by

recommender systems to combine different users' opinions and tastes in order to achieve personalized recommendations.

There are 3 approaches: Active or User Based Item Based Matrix Factorization

Figure 1: Intermediary entities (center) relate user to recommended item.

http://www.grouplens.org/system/files/vig-iui2009-tagsplanations.pdf

User-based method (1994, GroupLens)

Many people liked “Kung fuPanda”

– Can you tell how much I like it?

– The idea is to pick about 20-50 people who share similar taste with me, then how much I Like depend on how much THEY liked.

– In short: you may like it because your “friends” liked it

I have watched a lot of good &bad movies– Would you recommend me to watch “Avatar”?

– Here the idea is to pick from my previous list of 20-50 movies that are similar to “Avatar”, then how much I like it will depend on how highly rated those early movies

– In short: I may watch this movie because I have watched those movies … or

Item-based method (2001, deployed at Amazon)

Matrix Factorization (2006, Netflixchallenge)

Three different MF techniques are adopted:

Regularized MF Maximum margin MF Non-negative MF

Explicit versus implicit filtering

Within active and passive filtering there are explicit and implicit methods for determining user preferences.

Explicit collection of user preferences requires the evaluator to indicate a value for the content on a rating scale.

Implicit collection does not involve the direct input of opinion by the user, but instead it is assumed that their opinion is implied by their actions.

Significance of CF in Recommendation Engines

Scenario 1

The Wisdom of crowds and Law of Large Numbers suggest that as communities grow, it tends to make better decisions than a handful of editors.

Therefore the larger a community gets, the better its decisions will be. (voting system)

Implications

As the community grows, the news story that gets promoted to the front page will be of interest to the average community member.

This story maybe interesting, entertaining, and newsworthy information as judged by the community.

But not all the content will be directly relevant to your individual tastes.

Scenario 2 Here if we assume that we have enough data on:

Individual participants –likes/dislikes, voting habits, comments

and on how they collaborate or correlate with each other,

we can make predictions about what these users will like in the future based on what their tastes have been in the past, i.e. develop a collaboratively filtered recommendation engine.

Assumption: people's interests, preferences, and ideologies don't change too drastically.

Hence by collecting enough information on how you interact with the site and with other users, the (CF) system can recommend content to you.

This resolves the information overload problem where users are finding it increasingly difficult to locate the right information at the right time

The system finds the content and deliver it to you rather than it requiring you to scout for it.

Implications

Video

CF Limitations

Cold-Start Problem: is caused by new users in the system which have not submitted any ratings.

Sparse Data: for e.g. I have rated only one book at Amazon.

Easy attacks by malicious insiders—creating fake user profiles.

There's always a possibility of popularity bias.

Trust-aware Recommender Systems

Concern about the quality & reliability of every single rating.

In addition to profile-profile similarity, trustworthiness of a partner should also be considered.

Trust allows to base recommendations only on ratings given by users trusted directly by the current user or indirectly.

The system can ask the users to rate the other users.

A user can express her level of trust in another user she has interacted with.

If users are allowed to cast trust values on other users, then these values can be used to predict the trustworthiness of unknown users.

Users are allowed to explicitly express their web of trust.

Trust Network

Nodes are users and edges are trust statements. The red arrow represents an undefined and predictable trust statement.

http://gnuband.org/files/papers/trustaware_bootstrapping_of_recommender_systems_paolo_massa_wrs_ecai_2006/presentation

Local Trust Metrics Local metrics:

Predict (different) trust scores that are personalized from the point of view of every single user.

Pros: More accurate Attack resistance

Cons: Ignore “wisdom of the crowd” More complicated Computationally more expensive Must be computed for each user

ab

dc

1 5

32

?

Global Trust Metrics Global metrics:

Compute a single global trust value for every single user (reputation).

Predict how the community as a whole considers a certain user.

Pros: Based on the whole community opinion. Simple to compute.

Cons: Trust is subjective.

Epinions.com Example:

Consumer opinion site .

Users can review items ( cars, books, movies, software ).

Assign numeric ratings [1 (min) to 5 (max)] to items.

Users can also express:

- Web of Trust, i.e. reviewers whose reviews and ratings users have consistently found to be valuable.

- Block list, i.e. a list of authors whose reviews they found consistently offensive or inaccurate.

Trust metrics are attack-resistant.

Can be used to spot malicious users and take into account only reliable users and their ratings.

No global view of a reliable or trustworthy user.

A user can be considered trustworthy by one user and untrustworthy by another user.

http://sra.itc.it/people/massa/publications/massa_paolo_coopis_2004_trust-aware_Collaborative_Filtering_for_Recommender_Systems.pdf

The Long-Tail Theory

Every product has a buyer. However, it is difficult to bring right buyer and right seller together.

Most of the online sales (e-commerce and music) are done for the most popular products—this actually is a crucial problem.

If, non-popular products are brought to the interest of right buyers with a successful mechanism, profitability can increase drastically.

Companies like Amazon that apply the Long Tail effect successfully, make most of their profit not from the best selling products, but from the long tail part of the chart.

Usage of recommendation systems in ecommerce:

Personalized main page recommendation: building a main page for users according to their previous activity.

Recommending similar product or new products for the user.

Cross Sale: Demonstrating the products that are sold together.

Personalized Campaign/ advertisement / e-mail recommendations: Coming up automatically with the campaigns user might be interested in.

Advantages of a Recommender System

Unpopular products that are normally not advertised are introduced to buyers that might buy those products.

Calculated recommendations might be used to design customized pages for users or for email marketing.

Companies that use recommendation systems, have experienced a 10 to 35 percent increase in sales.

According to 2006 sales figures, 35% of Amazon’s sales are done through recommendation system.

http://en.webrazzi.com/2009/09/18/recommendation-systems-increasing-profit-by-long-tail/