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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. 3 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). 4 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.