Download Google Research Awards Proposal

Google Research Awards Proposal
Peter Christen (The Australian National University)
Scott Sanner (NICTA and the Australian National University)
Shengbo Guo (The Australian National University and NICTA)
Khoi-Nguyen Tran (The Australian National University)
1
Overview
1.1
Title: “Preference Elicitation for Social Recommendation”
1.2
Primary Principal Investigator
Peter Christen
Associate Dean (Higher Degree Research) and Senior Lecturer
ANU College of Engineering and Computer Science (CECS)
The Australian National University (ANU)
0200 Canberra, ACT, Australia
Email: [email protected]
Phone: +61 2 612 55690
Fax: +61 2 612 50010
WWW: http://cs.anu.edu.au/∼Peter.Christen
1.3
Google Contact and Sponsor
Doug Aberdeen, Google Zürich, [email protected]
2
2.1
Proposal
Abstract
Recommendation based on social networks, or social recommendation, is a fast growing
area of research that coincides with the explosive growth of popular social networking
sites such as Facebook. Whereas traditional recommender systems exploit features such
1
as item similarity, user similarity, collaborative tagging and ratings, and user history,
social networks provide a fifth and rich source of data for recommendation. For example,
social networks can be used to infer preferential similarities of friends/colleagues [1], or
to model influence propagation along social links [2]. While passive social recommender
systems have been proposed to exploit social network content [3], no social recommendation work addresses active preference elicitation that has the dual aims of providing
useful recommendations and optimally reducing uncertainty in a user’s preferences based
on relevance feedback from the user. Thus, we propose to develop novel preference elicitation methods for social recommendation and to perform a Facebook user study of
these methods in comparison to existing passive recommendation approaches.
2.2
Problem Statement and Research Goals
We ask the question: Do active preference elicitation approaches for social recommendation outperform passive approaches in terms of long-term average relevance accuracy?
We hypothesize the following: While active preference elicitation strategies based on
value of information may make initially suboptimal recommendations in their effort to
actively reduce uncertainty in a user’s preferences, the relevance feedback gained from
these recommendations (as measured by a user’s clicks or explicit ratings) will lead to
greater long-term average relevance accuracy vs. passive variants of the same algorithms.
If our evaluation demonstrates this hypothesis to be true in our Facebook user study, this
suggests a fundamentally new preference elicitation approach to social network-based
recommendation that differs from all currently proposed passive approaches.
2.3
Work Description and Expected Outcomes
The work will be split between the development of preference elicitation algorithms for
social recommendation, and an evaluation consisting of a Facebook-based user study. All
four project investigators will contribute to algorithmic development, while the majority
of the implementation and evaluation work will be conducted by Nguyen Tran.
The development of active preference elicitation algorithms based on value of information requires recommender systems that explicitly maintain a Bayesian model of uncertainty in a user’s utility. Given such a Bayesian utility model, the preference elicitation
approach is straightforward (e.g, [4]), so the main research lies in learning a Bayesian
model of utility jointly over user features, item features, and a social network. For
Bayesian utility learning, there are two attractive techniques that we wish to explore:
1. Bayesian graphical models with approximate inference: Methods such as SoRec [3]
provide a probabilistic (but non-Bayesian) way to integrate collaborative filtering
(CF) techniques with social network influence (SNI) models using probabilistic
matrix factorization for both. Alternative Bayesian graphical models for matrix
factorization have been proposed for CF alone in Matchbox [5]. We could extend
Matchbox additionally to a novel Bayesian matrix factorization for SNI, hence
providing a Bayesian version of SoRec that can be used for preference elicitation.
2. Gaussian Processes with graph kernels: Previous work [6] has provided a Gaussian
Process Bayesian preference elicitation approach using a joint kernel function that
factorizes over user and item features (with kernel parameters learned via autorelevance determination). This approach can be extended with an additional social
network-based graph kernel appropriate for social recommendation (e.g., [7]).
All code developed for this project will be publicly posted to Google Code.
For the user study evaluation, we will develop a Facebook app to collect information
provided by users’ wall posts containing URLs (URLs such as links to YouTube videos,
articles, blogs, picture albums, etc. provide an excellent source of items to recommend).
Collected information will include sender and recipient user identifiers, URL, post text,
comments, and time. We will also collect general wall post (and re-post) data in terms of
sender-recipient frequency and post text as a means of modeling social network influence
(c.f., [1]). Both proposed Bayesian utility modeling approaches can utilize this data.
We will invite all Computer Science and Engineering coursework students enrolled in
Sem. 1, 2011 to use our Facebook app for a period of one month. Prior to this, ethical
approval with be sought from the ANU Human Ethics Committee to conduct this user
study. Approximately 1300 students will be invited, leading to over 100 participants with
10% uptake. We will offer small gifts to random participtants to encourage participation.
The efficacy of the recommendation algorithms can be measured in the development
phase by evaluating recommendation accuracy on static datasets of the above Facebook
data that we will collect at the start of the project. Once the best candidate algorithms
are identified, we will proceed to the online evaluation phase, where we will select up
to two algorithms and run each in two modes: (a) passive mode, where the algorithm
simply recommends the top k URLs and (b) active preference elicitation mode, where
k recommended URLs are selected according to value of information as in [4]. Each
user will be randomly assigned one of the four algorithms, which will make k (≤ 10)
personalized URL recommendations per day. We will collect user clicks and explicit
relevance ratings for each recommendation to provide as relevance feedback to the respective algorithms and to plot each algorithm’s average click rate and rating over time.
2.4
Relation to Prior Work
While there exist works on Bayesian preference elicitation and recommendation [4, 5,
6, 8] (some our own) and various models of social recommendation and social network
influence propagation [1, 2, 3, 7, 9, 10] (to name some of the most representative works),
we are unaware of any previous work on Bayesian preference elicitation using value of
information heuristics for social recommendation. This proposal aims to combine and
unify some of these works in addition to completely novel research as outlined previously.
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Budget
Salary:
Start/End travels to Google Zürich:
Conference travel:
Student gifts for user study participation:
Total:
USD
USD
USD
USD
USD
62,661
6,400
4,100
600
73,661
Salary Justification The software engineering and Facebook user study for this research proposal is substantial and expected to take one full-time year. Given our teaching and research project staff allocations, we do not have the engineering resources at
the ANU or NICTA to devote one full-time staff member to this project for one year.
If this Google Faculty Research Award is successful, we plan to hire Mr Khoi-Nguyen
Tran. Nguyen is currently a PhD candidate on an Australian Postgraduate Award
(APA) scholarship. As this grant proposal is related to, but not directly aligned with
Nguyen’s PhD thesis topic on the Semantic Web, Nguyen would suspend his PhD candidature and work full-time on this proposed project for one year.
Nguyen will be employed as a research associate at the level appropriated for researcher
without a PhD, Academic Level A2. Yearly salary costs at level A2 are AUD 68,541 =
USD 62,661 (exchange rate from Google Currency Converter on 11 August 2011).
Travel We propose that Nguyen Tran will travel to Google Zürich at the beginning
and end of the project. The aim of the initial visit is to discuss the project with the
Google sponsor to get a better understanding of the aim of Google’s research interests in
social recommendation, while the second trip is aimed at presenting project outcomes.
Total cost is estimated at USD 3200 per trip × 2 trips: Flights Canberra to Switzerland,
Economy class return estimated at AUD 2,500 = USD 2300. Accommodation in Zürich
(for 4 nights) are estimated at USD 150 per night = USD 600 in total. Food and
incidentals (local travel, etc.) are estimated at USD 60 per day × 5 days = USD 300.
To present outcomes of this work, we aim to submit a research paper and attend one
of the top-international data mining or machine learning conferences, such as ACM
KDD, IEEE ICDM, ICML, etc. Total cost is estimated at USD 4100: Travel costs are
estimated at AUD 2,500 = USD 2,300: Conference registration is estimated at USD
800. Accommodation (for 5 nights) is estimated at USD 150 per night, thus USD 750
in total. Food and incidentals are estimated at USD 50 per day × 5 days = USD 250.
Other Small gifts to increase participation in Facebook experiment by ANU coursework students are estimated at USD 600 total: Apple IPod Touch - USD 250; Double
movie tickets USD 30 x 50 = USD 150; 2000 Facebook credits to be given to the most
active participants (assuming 10 credits for 1 USD).
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PI CVs
CVs for all authors are submitted with the proposal. Peter Christen is designated the
primary PI and contact. Additional information may be found at the authors’ webpages:
• Peter Christen:
http://cs.anu.edu.au/∼Peter.Christen/
• Scott Sanner:
http://users.cecs.anu.edu.au/∼ssanner/
• Shengbo Guo:
http://users.cecs.anu.edu.au/∼sguo/
• Khoi-Nguyen Tran:
http://cs.anu.edu.au/people/Nguyen.Tran/
References
[1] Parag Singla and Matthew Richardson. Yes, there is a correlation: from social
networks to personal behavior on the web. In WWW ’08: Proceedings of the 17th
International Conference on World Wide Web, pages 655–664, 2008.
[2] Duncan J. Watts and Peter S. Dodds. Influentials, networks, and public opinion
formation. Journal of Consumer Research, 34(4):441–458, December 2007.
[3] Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. SoRec: social recommendation using probabilistic matrix factorization. In CIKM ’08: Proceeding of the
17th ACM conference on Information and Knowledge Management, pages 931–940,
New York, NY, USA, 2008. ACM.
[4] Shengbo Guo and Scott Sanner. Real-time multiattribute Bayesian preference elicitation with pairwise comparison queries. In Thirteenth International Conference
on Artificial Intelligence and Statistics, 2010.
[5] David H. Stern, Ralf Herbrich, and Thore Graepel. Matchbox: large scale online
Bayesian recommendations. In WWW ’09: Proceedings of the 18th International
Conference on World Wide Web, pages 111–120, New York, NY, USA, 2009. ACM.
[6] Edwin Bonilla, Shengbo Guo, and Scott Sanner. Gaussian process preference elicitation, 2010. Submitted, available on request.
[7] François Fouss, Luh Yen, Alain Pirotte, and Marco Saerens. An experimental
investigation of graph kernels on a collaborative recommendation task. In ICDM,
pages 863–868, 2006.
[8] Craig Boutilier. A POMDP formulation of preference elicitation problems. In
Eighteenth National Conference on Artificial Intelligence, pages 239–246, Menlo
Park, CA, USA, 2002. American Association for Artificial Intelligence.
[9] Steffen Rendle, Leandro Balby Marinho, Alexandros Nanopoulos, and Lars
Schmidt-Thieme. Learning optimal ranking with tensor factorization for tag recommendation. In KDD ’09: Proceedings of the 15th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, pages 727–736, New York,
NY, USA, 2009. ACM.
[10] Jérôme Kunegis, Andreas Lommatzsch, and Christian Bauckhage. The Slashdot
zoo: mining a social network with negative edges. In WWW ’09: Proceedings of
the 18th International Conference on World Wide Web, pages 741–750, New York,
NY, USA, 2009. ACM.