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Modeling the Dynamics of Consumer Behavior from Massive Interaction Data
Recent technological innovations (e.g. e-commerce platforms, automated retail stores) have enabled dramatic changes in people's shopping experiences, as well as the accessibility to incredible volumes of consumer-product interaction data. As a result, machine learning (ML) systems can be widely developed to help people navigate relevant information and make decisions. Traditional ML systems have achieved great success on various well-defined problems such as speech recognition and facial recognition. Unlike these tasks where datasets and objectives are clearly benchmarked, modeling consumer behavior can be rather complicated; for example, consumer activities can be affected by real-time shopping contexts, collected interaction data can be noisy and biased, interests from multiple parties (both consumers and producers) can be involved in the predictive objectives.The primary goal of this dissertation is to address the obstacles in modeling consumer activities through computational approaches, but with careful considerations from economic and societal perspectives. Intellectually, such models help us to understand the forces that guide consumer behavior. Methodologically, I build algorithms capable of processing massive interaction datasets by connecting well-developed ML techniques and well-established economic theories. Practically, my work has applications ranging from recommender systems, e-commerce and business intelligence
A probabilistic model to resolve diversity-accuracy challenge of recommendation systems
Recommendation systems have wide-spread applications in both academia and
industry. Traditionally, performance of recommendation systems has been
measured by their precision. By introducing novelty and diversity as key
qualities in recommender systems, recently increasing attention has been
focused on this topic. Precision and novelty of recommendation are not in the
same direction, and practical systems should make a trade-off between these two
quantities. Thus, it is an important feature of a recommender system to make it
possible to adjust diversity and accuracy of the recommendations by tuning the
model. In this paper, we introduce a probabilistic structure to resolve the
diversity-accuracy dilemma in recommender systems. We propose a hybrid model
with adjustable level of diversity and precision such that one can perform this
by tuning a single parameter. The proposed recommendation model consists of two
models: one for maximization of the accuracy and the other one for
specification of the recommendation list to tastes of users. Our experiments on
two real datasets show the functionality of the model in resolving
accuracy-diversity dilemma and outperformance of the model over other classic
models. The proposed method could be extensively applied to real commercial
systems due to its low computational complexity and significant performance.Comment: 19 pages, 5 figure
Modeling Interdependent and Periodic Real-World Action Sequences
Mobile health applications, including those that track activities such as
exercise, sleep, and diet, are becoming widely used. Accurately predicting
human actions is essential for targeted recommendations that could improve our
health and for personalization of these applications. However, making such
predictions is extremely difficult due to the complexities of human behavior,
which consists of a large number of potential actions that vary over time,
depend on each other, and are periodic. Previous work has not jointly modeled
these dynamics and has largely focused on item consumption patterns instead of
broader types of behaviors such as eating, commuting or exercising. In this
work, we develop a novel statistical model for Time-varying, Interdependent,
and Periodic Action Sequences. Our approach is based on personalized,
multivariate temporal point processes that model time-varying action
propensities through a mixture of Gaussian intensities. Our model captures
short-term and long-term periodic interdependencies between actions through
Hawkes process-based self-excitations. We evaluate our approach on two activity
logging datasets comprising 12 million actions taken by 20 thousand users over
17 months. We demonstrate that our approach allows us to make successful
predictions of future user actions and their timing. Specifically, our model
improves predictions of actions, and their timing, over existing methods across
multiple datasets by up to 156%, and up to 37%, respectively. Performance
improvements are particularly large for relatively rare and periodic actions
such as walking and biking, improving over baselines by up to 256%. This
demonstrates that explicit modeling of dependencies and periodicities in
real-world behavior enables successful predictions of future actions, with
implications for modeling human behavior, app personalization, and targeting of
health interventions.Comment: Accepted at WWW 201
Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation
Session-based recommendation (SR) has become an important and popular
component of various e-commerce platforms, which aims to predict the next
interacted item based on a given session. Most of existing SR models only focus
on exploiting the consecutive items in a session interacted by a certain user,
to capture the transition pattern among the items. Although some of them have
been proven effective, the following two insights are often neglected. First, a
user's micro-behaviors, such as the manner in which the user locates an item,
the activities that the user commits on an item (e.g., reading comments, adding
to cart), offer fine-grained and deep understanding of the user's preference.
Second, the item attributes, also known as item knowledge, provide side
information to model the transition pattern among interacted items and
alleviate the data sparsity problem. These insights motivate us to propose a
novel SR model MKM-SR in this paper, which incorporates user Micro-behaviors
and item Knowledge into Multi-task learning for Session-based Recommendation.
Specifically, a given session is modeled on micro-behavior level in MKM-SR,
i.e., with a sequence of item-operation pairs rather than a sequence of items,
to capture the transition pattern in the session sufficiently. Furthermore, we
propose a multi-task learning paradigm to involve learning knowledge embeddings
which plays a role as an auxiliary task to promote the major task of SR. It
enables our model to obtain better session representations, resulting in more
precise SR recommendation results. The extensive evaluations on two benchmark
datasets demonstrate MKM-SR's superiority over the state-of-the-art SR models,
justifying the strategy of incorporating knowledge learning
Memory Augmented Graph Neural Networks for Sequential Recommendation
The chronological order of user-item interactions can reveal time-evolving
and sequential user behaviors in many recommender systems. The items that users
will interact with may depend on the items accessed in the past. However, the
substantial increase of users and items makes sequential recommender systems
still face non-trivial challenges: (1) the hardness of modeling the short-term
user interests; (2) the difficulty of capturing the long-term user interests;
(3) the effective modeling of item co-occurrence patterns. To tackle these
challenges, we propose a memory augmented graph neural network (MA-GNN) to
capture both the long- and short-term user interests. Specifically, we apply a
graph neural network to model the item contextual information within a
short-term period and utilize a shared memory network to capture the long-range
dependencies between items. In addition to the modeling of user interests, we
employ a bilinear function to capture the co-occurrence patterns of related
items. We extensively evaluate our model on five real-world datasets, comparing
with several state-of-the-art methods and using a variety of performance
metrics. The experimental results demonstrate the effectiveness of our model
for the task of Top-K sequential recommendation.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI
2020
Trust networks for recommender systems
Recommender systems use information about their user’s profiles and relationships to suggest items that might be of interest to them. Recommenders that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional systems, provided they succeed in utilizing the additional (dis)trust information to their advantage. Such trust-enhanced recommenders consist of two main components: recommendation technologies and trust metrics (techniques which aim to estimate the trust between two unknown users.)
We introduce a new bilattice-based model that considers trust and distrust as two different but dependent components, and study the accompanying trust metrics. Two of their key building blocks are trust propagation and aggregation. If user a wants to form an opinion about an unknown user x, a can contact one of his acquaintances, who can contact another one, etc., until a user is reached who is connected with x (propagation). Since a will often contact several persons, one also needs a mechanism to combine the trust scores that result from several propagation paths (aggregation). We introduce new fuzzy logic propagation operators and focus on the potential of OWA strategies and the effect of knowledge defects. Our experiments demonstrate that propagators that actively incorporate distrust are more accurate than standard approaches, and that new aggregators result in better predictions than purely bilattice-based operators.
In the second part of the dissertation, we focus on the application of trust networks in recommender systems. After the introduction of a new detection measure for controversial items, we show that trust-based approaches are more effective than baselines. We also propose a new algorithm that achieves an immediate high coverage while the accuracy remains adequate. Furthermore, we also provide the first experimental study on the potential of distrust in a memory-based collaborative filtering recommendation process. Finally, we also study the user cold start problem; we propose to identify key figures in the network, and to suggest them as possible connection points for newcomers. Our experiments show that it is much more beneficial for a new user to connect to an identified key figure instead of making random connections
How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements
We investigate a growing body of work that seeks to improve recommender
systems through the use of review text. Generally, these papers argue that
since reviews 'explain' users' opinions, they ought to be useful to infer the
underlying dimensions that predict ratings or purchases. Schemes to incorporate
reviews range from simple regularizers to neural network approaches. Our
initial findings reveal several discrepancies in reported results, partly due
to (e.g.) copying results across papers despite changes in experimental
settings or data pre-processing. First, we attempt a comprehensive analysis to
resolve these ambiguities. Further investigation calls for discussion on a much
larger problem about the "importance" of user reviews for recommendation.
Through a wide range of experiments, we observe several cases where
state-of-the-art methods fail to outperform existing baselines, especially as
we deviate from a few narrowly-defined settings where reviews are useful. We
conclude by providing hypotheses for our observations, that seek to
characterize under what conditions reviews are likely to be helpful. Through
this work, we aim to evaluate the direction in which the field is progressing
and encourage robust empirical evaluation.Comment: 4 pages, 3 figures. Accepted for publication at SIGIR '2
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