Sedhain, Suvash
Description
Recommending a personalised list of items to users is a core task
for many online services such as Amazon, Netflix, and Youtube.
Recommender systems are the algorithms that facilitate such
personalised recommendation. Collaborative filtering (CF), the
most popular class of recommendation algorithm, exploits the
wisdom of the crowd by predicting users’ preferences not only
from her past actions but also from the preferences of other
like-minded users. In...[Show more] general, it is desirable to have a CF
framework that is (1) applicable to wide range of recommendation
scenarios, (2) learning-based, (3) amenable to convex
optimisation, and (4) scalable. However, all existing CF methods,
such as neighbourhood and matrix factorisation, lack one or more
of these desiderata.
In this dissertation, we investigate linear models, an
under-appreciated but promising area for recommendations that
addresses all the above desiderata. We formulate a unified
framework based on linear models that yields CF algorithms for
four prevalent scenarios. First, we investigate Social CF, which
involves leveraging users’ signals from online social networks.
We propose social affinity filtering (SAF), that exploits
fine-grained user interactions and activities in a social
network. Second, we investigate Cold-Start CF, which refers to
the scenario when we do not have any historical data about a user
or an item. We formulate a large-scale linear model that
leverages users social information. Third, we investigate
One-Class CF, which concerns suggesting relevant items to users
from the data that consists of only positive preferences such as
item purchase.Noting the superior performance of linear models,
we propose LRec, a user-focused linear CF model, and extend it to
large-scale datasets via dimensionality reduction. Finally, we
investigate Explicit Feedback CF, which concerns predicting
user’s actual preferences such as rating, or like/dislikes. We
identify CF as an auto-encoding problem and propose AUTOREC, a
generalized neural network architecture for CF. We demonstrate
state-of-the-art performance of the proposed models through
extensive experimentation on real world datasets.
In a nutshell, this dissertation elucidates the power of linear
models for various CF tasks and paves the way for further
research on applying deep learning models to CF.
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