ubs web 2.0 contest: recommender systems for financial institutes
DESCRIPTION
Winning Submission "Collaborative Filtering - A Driver to Enable Clients to Explore, Share Experience and Build Recommendations for Products & Services" outlines how the modern algorithms exploiting the wisdom of crowds can be used to: - create adequate bundles of products and services - allows for dynamic and fast adoption of changes to a client's life situation - provide the means for information/content sharing among clients and between clients and advisors - facilitates interaction with information about products and services.TRANSCRIPT
Collaborative FilteringA Driver to Enable Clients to Explore, Share Experience and Build Recommendations for Products & Services
Web 2.0 UBS Contest 2009Zurich, Switzerland
Amancio Bouza
Web 2.0 UBS Contest 2009Dec. 2009
User model of the Web 2.0
like to share experiences and to generate feedback
like to generate content and to contribute
want to be part of something bigger
trust other users more then experts based on the Wisdom of Crowds assumption
are intrinsic motivated
are connected everywhere and every time
do not honor guided help of experts or systems
want do discover and explore
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Web 2.0 UBS Contest 2009Dec. 2009
External Events & Motivation
Participants & Information
SystemsProducts &
Services
Challenges Advisor
is not aware of what client worries and about the clients needs, changes in client’s life situation
recommends products & services based on limited information, i.e. client information / client portfolio
Client
is challenged with domain vocabulary, financial news, global events and has generally no domain knowledge
does not trust advisor, but other clients (Principal Agents Theory)
depends totally on advisor
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Banc products
Banc servicesNews
Financial Market Data
Client Information
Banc marketing
Advisor
Client
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Private events
...
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‣Challenges
‣no creation of adequate bundle of products & services for clients
‣Latency in adoption to changes in client’s life situation
‣No explicit/implicit or consolidated experience sharing among clients
‣No interaction with available information and products & services
Web 2.0 UBS Contest 2009Dec. 2009
Collaborative Filtering Collaborative filtering (CF)
is a modern approach to build recommender systems
is a new & trendy search paradigm: The item finds the client instead of letting the client find the item
personalizes the content and filters only relevant products
goes further then Wisdom of Crowds. CF relies on Wisdom of Community of Interests (COI).
generates more relevant recommendations then Content Filtering by considering the past actions and experiences of other clients
Two main approaches:
User-based CF: Clients who share the same preferences continue to do so in the future
Items are recommended that are preferred by clients with similar preferences, similar client portfolio or similar product & service ratings. (similar to Last.fm: “User X likes similar music, look what he also likes”)
Item-based CF: Latent semantics exists when items are combined significantly often by clients
If a client is interested in an item then other items are recommended that were combined significantly often with the current item of interest (similar to Amazon.com: “Clients that bought product X also bought product Y”)
The Web 2.0 idea of CF
User like to share ratings and experiences by giving feedback such as ratings or reviews for items
The more the users participate the more relevant recommendations can be provided by the recommender systems. Therefore, users have an interest in sharing their ratings
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Web 2.0 UBS Contest 2009Dec. 2009
‣Benefits
‣creation of adequate bundle of products & services for clients
‣Dynamic and fast adoption of changes in client’s life situation
‣Information/content sharing among clients and between clients and advisors
‣interaction with available with information and products & services
External Events & Motivation
Participants & Information
SystemsProducts &
Services
Banc Marketing
Financial Market Data
News
Advisor
Recommender System
Banc services
Client
Banc products
Private events
...
...
General Approach based on Collaborative Filtering & Web 2.0
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Advisor & Client
collaborate on shared common knowledge
develop together a better bundle based on given recommendations
Advisor
supports the client with domain knowledge of an expert
helps to interpret recommendations and its consequences
gets proper understanding of what client worries and client’s need
Client
is more independent of advisor and explores possibilities based on recommendations
makes decisions based on recommendations
trusts the combination of other client’s experience and advisor’s suggestions
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Web 2.0 UBS Contest 2009Dec. 2009
Architecture Recommendations are either used by the client to make the decision what to buy or as a basis for discussion with an advisor
Client’s life situation and goals can be discussed together with advisor
Financial market situation are used to validate recommendations
Advisor gains knowledge of client specific interests and what he may be interested in addition.
Similar users are computed by either applying traditional data mining techniques such as cluster algorithms or kNN approaches
Recommendation: Go for clustering because of scalability
Similar items are computed based the analysis how products & services have been combined in the past
Recommendation: Go for (incremental) Single Value Decomposition approaches because that’s the state-of-the-art in collaborative filtering
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Products & Services Client Ratings
Computation of Client Similarities
Computation of latent semantics between Products & Services
Data
Client similarityItem similarity
Presentation of Recommendations
Collaboration with Advisor
Request of new Recommendations
Consumption of Product & ServicesEvent
Implementation:1. Clustering of similar users based on ratings to get Community of Interests (COI)2. k nearest neighbor (kNN) to compute the k most similar users
Implementation:1. Single Value Decomposition (SVD)2. Association rule mining to compute3. Bayesian Networks to compute probability that two items are a
good bundle
Web 2.0 UBS Contest 2009Dec. 2009
MobileInternet Face-to-Face
Interaction Landscape
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Collaboration between Client and Advisor on Microsoft’s Surface Touchtable
Our recommendations
Product APeople that have a similar portfolio also bought Product A
More recommendations
presentation on the UBS Web page
Product A
Product B
Product C
Product X buy
Product Y buy
Product Z buy
Our recommendations
Your ratings
contact advisor
Browse and rate recommendations on Smart Phones
Web 2.0 UBS Contest 2009Dec. 2009
Further Benefits for the UBS
Increased client loyalty due to client’s transaction costs (lock-in)
Increased client satisfaction because of more personalized and more relevant products & services bundles. May also lead to increased client loyalty
Clients discover new interesting products & services they never considered because these have been recommended by the recommender system. Leads to new investments by the client
Increase of sales because advisors can look for new products & services that a client would be interest in. Advisor does know what a client wants before the client does
First mover advantage because a recommender system needs client feedback before performing well
Further analysis of upcoming trends based on significant shifts in item good item combinations
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Web 2.0 UBS Contest 2009Dec. 2009
Management summary
Recommendations help people to explore and discover new products & services
Recommendations can support the collaboration between client and advisor
Recommendations make people independent and motivate people to buy
People trust other people and the Wisdom of Crowds
Collaborative filtering is a modern approach of building a high quality recommender system
With collaborative filtering the products & services find the right user instead of letting the user find the relevant products & services
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