3,791 research outputs found
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
Towards Question-based Recommender Systems
Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.Comment: accepted by SIGIR 202
What Else Would I Like? A User Simulator using Alternatives for Improved Evaluation of Fashion Conversational Recommendation Systems
In Conversational Recommendation Systems (CRS), a user can provide feedback
on recommended items at each interaction turn, leading the CRS towards more
desirable recommendations. Currently, different types of CRS offer various
possibilities for feedback, i.e., natural language feedback, or answering
clarifying questions. In most cases, a user simulator is employed for training
as well as evaluating the CRS. Such user simulators typically critique the
current retrieved items based on knowledge of a single target item. Still,
evaluating systems in offline settings with simulators suffers from problems,
such as focusing entirely on a single target item (not addressing the
exploratory nature of a recommender system), and exhibiting extreme patience
(consistent feedback over a large number of turns). To overcome these
limitations, we obtain extra judgements for a selection of alternative items in
common CRS datasets, namely Shoes and Fashion IQ Dresses. Going further, we
propose improved user simulators that allow simulated users not only to express
their preferences about alternative items to their original target, but also to
change their mind and level of patience. In our experiments using the relative
image captioning CRS setting and different CRS models, we find that using the
knowledge of alternatives by the simulator can have a considerable impact on
the evaluation of existing CRS models, specifically that the existing
single-target evaluation underestimates their effectiveness, and when simulated
users are allowed to instead consider alternatives, the system can rapidly
respond to more quickly satisfy the user
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook
In recent years, reinforcement learning and bandits have transformed a wide
range of real-world applications including healthcare, finance, recommendation
systems, robotics, and last but not least, the speech and natural language
processing. While most speech and language applications of reinforcement
learning algorithms are centered around improving the training of deep neural
networks with its flexible optimization properties, there are still many
grounds to explore to utilize the benefits of reinforcement learning, such as
its reward-driven adaptability, state representations, temporal structures and
generalizability. In this survey, we present an overview of recent advancements
of reinforcement learning and bandits, and discuss how they can be effectively
employed to solve speech and natural language processing problems with models
that are adaptive, interactive and scalable.Comment: To appear in Expert Systems with Applications. Accompanying
INTERSPEECH 2022 Tutorial on the same topic. Including latest advancements in
large language models (LLMs
Reinforcement Learning for Generative AI: A Survey
Deep Generative AI has been a long-standing essential topic in the machine
learning community, which can impact a number of application areas like text
generation and computer vision. The major paradigm to train a generative model
is maximum likelihood estimation, which pushes the learner to capture and
approximate the target data distribution by decreasing the divergence between
the model distribution and the target distribution. This formulation
successfully establishes the objective of generative tasks, while it is
incapable of satisfying all the requirements that a user might expect from a
generative model. Reinforcement learning, serving as a competitive option to
inject new training signals by creating new objectives that exploit novel
signals, has demonstrated its power and flexibility to incorporate human
inductive bias from multiple angles, such as adversarial learning,
hand-designed rules and learned reward model to build a performant model.
Thereby, reinforcement learning has become a trending research field and has
stretched the limits of generative AI in both model design and application. It
is reasonable to summarize and conclude advances in recent years with a
comprehensive review. Although there are surveys in different application areas
recently, this survey aims to shed light on a high-level review that spans a
range of application areas. We provide a rigorous taxonomy in this area and
make sufficient coverage on various models and applications. Notably, we also
surveyed the fast-developing large language model area. We conclude this survey
by showing the potential directions that might tackle the limit of current
models and expand the frontiers for generative AI
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