3 research outputs found
Monotone Retargeting for Unsupervised Rank Aggregation with Object Features
Learning the true ordering between objects by aggregating a set of expert
opinion rank order lists is an important and ubiquitous problem in many
applications ranging from social choice theory to natural language processing
and search aggregation. We study the problem of unsupervised rank aggregation
where no ground truth ordering information in available, neither about the true
preference ordering between any set of objects nor about the quality of
individual rank lists. Aggregating the often inconsistent and poor quality rank
lists in such an unsupervised manner is a highly challenging problem, and
standard consensus-based methods are often ill-defined, and difficult to solve.
In this manuscript we propose a novel framework to bypass these issues by using
object attributes to augment the standard rank aggregation framework. We design
algorithms that learn joint models on both rank lists and object features to
obtain an aggregated rank ordering that is more accurate and robust, and also
helps weed out rank lists of dubious validity. We validate our techniques on
synthetic datasets where our algorithm is able to estimate the true rank
ordering even when the rank lists are corrupted. Experiments on three real
datasets, MQ2008, MQ2008 and OHSUMED, show that using object features can
result in significant improvement in performance over existing rank aggregation
methods that do not use object information. Furthermore, when at least some of
the rank lists are of high quality, our methods are able to effectively exploit
their high expertise to output an aggregated rank ordering of great accuracy.Comment: 15 pages, 2 figures, 1 tabl
Clustered Monotone Transforms for Rating Factorization
Exploiting low-rank structure of the user-item rating matrix has been the
crux of many recommendation engines. However, existing recommendation engines
force raters with heterogeneous behavior profiles to map their intrinsic rating
scales to a common rating scale (e.g. 1-5). This non-linear transformation of
the rating scale shatters the low-rank structure of the rating matrix,
therefore resulting in a poor fit and consequentially, poor recommendations. In
this paper, we propose Clustered Monotone Transforms for Rating Factorization
(CMTRF), a novel approach to perform regression up to unknown monotonic
transforms over unknown population segments. Essentially, for recommendation
systems, the technique searches for monotonic transformations of the rating
scales resulting in a better fit. This is combined with an underlying matrix
factorization regression model that couples the user-wise ratings to exploit
shared low dimensional structure. The rating scale transformations can be
generated for each user, for a cluster of users, or for all the users at once,
forming the basis of three simple and efficient algorithms proposed in this
paper, all of which alternate between transformation of the rating scales and
matrix factorization regression. Despite the non-convexity, CMTRF is
theoretically shown to recover a unique solution under mild conditions.
Experimental results on two synthetic and seven real-world datasets show that
CMTRF outperforms other state-of-the-art baselines.Comment: The first two authors contributed equally to the paper. The paper to
appear in WSDM 201
Advances in Collaborative Filtering and Ranking
In this dissertation, we cover some recent advances in collaborative
filtering and ranking. In chapter 1, we give a brief introduction of the
history and the current landscape of collaborative filtering and ranking;
chapter 2 we first talk about pointwise collaborative filtering problem with
graph information, and how our proposed new method can encode very deep graph
information which helps four existing graph collaborative filtering algorithms;
chapter 3 is on the pairwise approach for collaborative ranking and how we
speed up the algorithm to near-linear time complexity; chapter 4 is on the new
listwise approach for collaborative ranking and how the listwise approach is a
better choice of loss for both explicit and implicit feedback over pointwise
and pairwise loss; chapter 5 is about the new regularization technique
Stochastic Shared Embeddings (SSE) we proposed for embedding layers and how it
is both theoretically sound and empirically effectively for 6 different tasks
across recommendation and natural language processing; chapter 6 is how we
introduce personalization for the state-of-the-art sequential recommendation
model with the help of SSE, which plays an important role in preventing our
personalized model from overfitting to the training data; chapter 7, we
summarize what we have achieved so far and predict what the future directions
can be; chapter 8 is the appendix to all the chapters.Comment: PhD Dissertation 202