Scott Sanner

Improving LDA topic models for microblogs via tweet pooling and automatic labeling

Twitter, or the world of 140 characters poses serious challenges to the efficacy of topic models on short, messy text. While topic models such as Latent Dirichlet Allocation (LDA) have a long history of successful application to news articles and academic abstracts, they are often less coherent when applied to microblog content like Twitter. In this paper, we investigate methods to improve topics learned from Twitter content without modifying the basic machinery of LDA; we achieve this through various pooling schemes that aggregate tweets in a data preprocessing step for LDA. We empirically establish that a novel method of tweet pooling by hashtags leads to a vast improvement in a variety of measures for topic coherence across three diverse Twitter datasets in comparison to an unmodified LDA baseline and a variety of pooling schemes. An additional contribution of automatic hashtag labeling further improves on the hashtag pooling results for a subset of metrics. Overall, these two novel schemes lead to significantly improved LDA topic models on Twitter content.

A Survey of the Seventh International Planning Competition

In this article we review the 2011 International Planning Competition. We give an overview of the history of the competition, discussing how it has developed since its first edition in 1998. The 2011 competition was run in three main separate tracks: the deterministic (classical) track; the learning track; and the uncertainty track. Each track proposed its own distinct set of new challenges and the participants rose to these admirably, the results of each track showing promising progress in each area. The competition attracted a record number of participants this year, showing its continued and strong position as a major central pillar of the international planning research community.

AutoRec: Autoencoders Meet Collaborative Filtering

This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRecs compact and efficiently trainable model outperforms state-of-the-art CF techniques (biased matrix factorization, RBM-CF and LLORMA) on the Movielens and Netflix datasets.

Practical solution techniques for first-order MDPs

Many traditional solution approaches to relationally specified decision-theoretic planning problems (e.g., those stated in the probabilistic planning domain description language, or PPDDL) ground the specification with respect to a specific instantiation of domain objects and apply a solution approach directly to the resulting ground Markov decision process (MDP). Unfortunately, the space and time complexity of these grounded solution approaches are polynomial in the number of domain objects and exponential in the predicate arity and the number of nested quantifiers in the relational problem specification. An alternative to grounding a relational planning problem is to tackle the problem directly at the relational level. In this article, we propose one such approach that translates an expressive subset of the PPDDL representation to a first-order MDP (FOMDP) specification and then derives a domain-independent policy without grounding at any intermediate step. However, such generality does not come without its own set of challenges-the purpose of this article is to explore practical solution techniques for solving FOMDPs. To demonstrate the applicability of our techniques, we present proof-of-concept results of our first-order approximate linear programming (FOALP) planner on problems from the probabilistic track of the ICAPS 2004 and 2006 International Planning Competitions.

New objective functions for social collaborative filtering

This paper examines the problem of social collaborative filtering (CF) to recommend items of interest to users in a social network setting. Unlike standard CF algorithms using relatively simple user and item features, recommendation in social networks poses the more complex problem of learning user preferences from a rich and complex set of user profile and interaction information. Many existing social CF methods have extended traditional CF matrix factorization, but have overlooked important aspects germane to the social setting. We propose a unified framework for social CF matrix factorization by introducing novel objective functions for training. Our new objective functions have three key features that address main drawbacks of existing approaches: (a) we fully exploit feature-based user similarity, (b) we permit direct learning of user-to-user information diffusion, and (c) we leverage co-preference (dis)agreement between two users to learn restricted areas of common interest. We evaluate these new social CF objectives, comparing them to each other and to a variety of (social) CF baselines, and analyze user behavior on live user trials in a custom-developed Facebook App involving data collected over five months from over 100 App users and their 37,000+ friends.

Social collaborative filtering for cold-start recommendations

We examine the cold-start recommendation task in an online retail setting for users who have not yet purchased (or interacted in a meaningful way with) any available items but who have granted access to limited side information, such as basic demographic data (gender, age, location) or social network information (Facebook friends or page likes). We formalize neighborhood-based methods for cold-start collaborative filtering in a generalized matrix algebra framework that does not require purchase data for target users when their side information is available. In real-data experiments with 30,000 users who purchased 80,000+ books and had 9,000,000+ Facebook friends and 6,000,000+ page likes, we show that using Facebook page likes for cold-start recommendation yields up to a 3-fold improvement in mean average precision (mAP) and up to 6-fold improvements in Precision@k and Recall@k compared to most-popular-item, demographic, and Facebook friend cold-start recommenders. These results demonstrate the substantial predictive power of social network content, and its significant utility in a challenging problem - recommendation for cold-start users.

Probabilistic latent maximal marginal relevance

Diversity has been heavily motivated in the information retrieval literature as an objective criterion for result sets in search and recommender systems. Perhaps one of the most well-known and most used algorithms for result set diversification is that of Maximal Marginal Relevance (MMR). In this paper, we show that while MMR is somewhat ad-hoc and motivated from a purely pragmatic perspective, we can derive a more principled variant via probabilistic inference in a latent variable graphical model. This novel derivation presents a formal probabilistic latent view of MMR (PLMMR) that (a) removes the need to manually balance relevance and diversity parameters, (b) shows that specific definitions of relevance and diversity metrics appropriate to MMR emerge naturally, and (c) formally derives variants of latent semantic indexing (LSI) similarity metrics for use in PLMMR. Empirically, PLMMR outperforms MMR with standard term frequency based similarity and diversity metrics since PLMMR maximizes latent diversity in the results.

Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity

Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption. Recent work analyzing massive datasets advances our understanding of popularity, but one major gap remains: To precisely quantify the relationship between the popularity of an online item and the external promotions it receives. This work supplies the missing link between exogenous inputs from public social media platforms, such as Twitter, and endogenous responses within the content platform, such as YouTube. We develop a novel mathematical model, the Hawkes intensity process, which can explain the complex popularity history of each video according to its type of content, network of diffusion, and sensitivity to promotion. Our model supplies a prototypical description of videos, called an endo-exo map. This map explains popularity as the result of an extrinsic factor -- the amount of promotions from the outside world that the video receives, acting upon two intrinsic factors -- sensitivity to promotion, and inherent virality. We use this model to forecast future popularity given promotions on a large 5-months feed of the most-tweeted videos, and found it to lower the average error by 28.6% from approaches based on popularity history. Finally, we can identify videos that have a high potential to become viral, as well as those for which promotions will have hardly any effect.Explaining and predicting the popularity of online multimedia content is an important problem for the practice of information dissemination and consumption. Recent work advances our understanding of popularity, but one important gap remains: to precisely quantify the relationship between the popularity of an online item and the external promotions it receives. This work supplies the missing link between exogenous inputs from public social media platforms (Twitter) and endogenous responses within video content platforms (Youtube). This is done via a novel mathematical model, the Hawkes intensity process, which is able to explain the complex popularity history of each video according to its content, network, and sensitivity to promotion. This model supplies a prototypical description of videos, called an endo-exo map, which allows us to explain the popularity as the joint effects of two intrinsic measures and the amount of discussions from the outside world. This model also allows us to forecast the effects of future promotions more accurately than approaches based on popularity history alone, and to identify videos that have a high potential to become viral, or those for which promotions will have hardly any effect.

Multi-evidence lifted message passing, with application to PageRank and the Kalman filter

Lifted message passing algorithms exploit repeated structure within a given graphical model to answer queries efficiently. Given evidence, they construct a lifted network of supernodes and superpotentials corresponding to sets of nodes and potentials that are indistinguishable given the evidence. Recently, efficient algorithms were presented for updating the structure of an existing lifted network with incremental changes to the evidence. In the inference stage, however, current algorithms need to construct a separate lifted network for each evidence case and run a modified message passing algorithm on each lifted network separately. Consequently, symmetries across the inference tasks are not exploited. In this paper, we present a novel lifted message passing technique that exploits symmetries across multiple evidence cases. The benefits of this multi-evidence lifted inference are shown for several important AI tasks such as computing personalized PageRanks and Kalman filters via multievidence lifted Gaussian belief propagation.

Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model

It has been previously observed that optimization of the 1-call@k relevance objective (i.e., a set-based objective that is 1 if at least one document is relevant, otherwise 0) empirically correlates with diverse retrieval. In this paper, we proceed one step further and show theoretically that greedily optimizing expected 1-call@k w.r.t. a latent subtopic model of binary relevance leads to a diverse retrieval algorithm sharing many features of existing diversification approaches. This new result is complementary to a variety of diverse retrieval algorithms derived from alternate rank-based relevance criteria such as average precision and reciprocal rank. As such, the derivation presented here for expected 1-call@k provides a novel theoretical perspective on the emergence of diversity via a latent subtopic model of relevance --- an idea underlying both ambiguous and faceted subtopic retrieval that have been used to motivate diverse retrieval.