472 research outputs found
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
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Static recommendation methods like collaborative filtering suffer from the
inherent limitation of performing real-time personalization for cold-start
users. Online recommendation, e.g., multi-armed bandit approach, addresses this
limitation by interactively exploring user preference online and pursuing the
exploration-exploitation (EE) trade-off. However, existing bandit-based methods
model recommendation actions homogeneously. Specifically, they only consider
the items as the arms, being incapable of handling the item attributes, which
naturally provide interpretable information of user's current demands and can
effectively filter out undesired items. In this work, we consider the
conversational recommendation for cold-start users, where a system can both ask
the attributes from and recommend items to a user interactively. This important
scenario was studied in a recent work. However, it employs a hand-crafted
function to decide when to ask attributes or make recommendations. Such
separate modeling of attributes and items makes the effectiveness of the system
highly rely on the choice of the hand-crafted function, thus introducing
fragility to the system. To address this limitation, we seamlessly unify
attributes and items in the same arm space and achieve their EE trade-offs
automatically using the framework of Thompson Sampling. Our Conversational
Thompson Sampling (ConTS) model holistically solves all questions in
conversational recommendation by choosing the arm with the maximal reward to
play. Extensive experiments on three benchmark datasets show that ConTS
outperforms the state-of-the-art methods Conversational UCB (ConUCB) and
Estimation-Action-Reflection model in both metrics of success rate and average
number of conversation turns.Comment: TOIS 202
Enhancing User Personalization in Conversational Recommenders
Conversational recommenders are emerging as a powerful tool to personalize a
user's recommendation experience. Through a back-and-forth dialogue, users can
quickly hone in on just the right items. Many approaches to conversational
recommendation, however, only partially explore the user preference space and
make limiting assumptions about how user feedback can be best incorporated,
resulting in long dialogues and poor recommendation performance. In this paper,
we propose a novel conversational recommendation framework with two unique
features: (i) a greedy NDCG attribute selector, to enhance user personalization
in the interactive preference elicitation process by prioritizing attributes
that most effectively represent the actual preference space of the user; and
(ii) a user representation refiner, to effectively fuse together the user
preferences collected from the interactive elicitation process to obtain a more
personalized understanding of the user. Through extensive experiments on four
frequently used datasets, we find the proposed framework not only outperforms
all the state-of-the-art conversational recommenders (in terms of both
recommendation performance and conversation efficiency), but also provides a
more personalized experience for the user under the proposed multi-groundtruth
multi-round conversational recommendation setting.Comment: To Appear On TheWebConf (WWW) 202
An investigation on the impact of natural language on conversational recommendations
In this paper, we investigate the combination of Virtual Assistants and Conversational Recommender Systems (CoRSs) by designing and implementing a framework named ConveRSE, for building chatbots that can recommend items from different domains and interact with the user through natural language. An user experiment was carried out to understand how natural language influences both the cost of interaction and recommendation accuracy of a CoRS. Experimental results show that natural language can indeed improve user experience, but some critical aspects of the interaction should be mitigated appropriately
Comparative preferences induction methods for conversational recommenders
In an era of overwhelming choices, recommender systems aim at recommending the most suitable items to the user. Preference handling is one of the core issues in the design of recommender systems and so it is important for them to catch and model the user’s preferences as accurately as possible. In previous work, comparative preferences-based patterns were developed to handle preferences deduced by the system. These patterns assume there are only two values for each feature. However, real-world features can be multi-valued. In this paper, we develop preference induction methods which aim at capturing several preference nuances from the user feedback when features have more than two values. We prove the efficiency of the proposed methods through an experimental study
Explainable Active Learning for Preference Elicitation
Gaining insights into the preferences of new users and subsequently
personalizing recommendations necessitate managing user interactions
intelligently, namely, posing pertinent questions to elicit valuable
information effectively. In this study, our focus is on a specific scenario of
the cold-start problem, where the recommendation system lacks adequate user
presence or access to other users' data is restricted, obstructing employing
user profiling methods utilizing existing data in the system. We employ Active
Learning (AL) to solve the addressed problem with the objective of maximizing
information acquisition with minimal user effort. AL operates for selecting
informative data from a large unlabeled set to inquire an oracle to label them
and eventually updating a machine learning (ML) model. We operate AL in an
integrated process of unsupervised, semi-supervised, and supervised ML within
an explanatory preference elicitation process. It harvests user feedback (given
for the system's explanations on the presented items) over informative samples
to update an underlying ML model estimating user preferences. The designed user
interaction facilitates personalizing the system by incorporating user feedback
into the ML model and also enhances user trust by refining the system's
explanations on recommendations. We implement the proposed preference
elicitation methodology for food recommendation. We conducted human experiments
to assess its efficacy in the short term and also experimented with several AL
strategies over synthetic user profiles that we created for two food datasets,
aiming for long-term performance analysis. The experimental results demonstrate
the efficiency of the proposed preference elicitation with limited user-labeled
data while also enhancing user trust through accurate explanations.Comment: Preprin
Constructive Preference Elicitation over Hybrid Combinatorial Spaces
Preference elicitation is the task of suggesting a highly preferred
configuration to a decision maker. The preferences are typically learned by
querying the user for choice feedback over pairs or sets of objects. In its
constructive variant, new objects are synthesized "from scratch" by maximizing
an estimate of the user utility over a combinatorial (possibly infinite) space
of candidates. In the constructive setting, most existing elicitation
techniques fail because they rely on exhaustive enumeration of the candidates.
A previous solution explicitly designed for constructive tasks comes with no
formal performance guarantees, and can be very expensive in (or unapplicable
to) problems with non-Boolean attributes. We propose the Choice Perceptron, a
Perceptron-like algorithm for learning user preferences from set-wise choice
feedback over constructive domains and hybrid Boolean-numeric feature spaces.
We provide a theoretical analysis on the attained regret that holds for a large
class of query selection strategies, and devise a heuristic strategy that aims
at optimizing the regret in practice. Finally, we demonstrate its effectiveness
by empirical evaluation against existing competitors on constructive scenarios
of increasing complexity.Comment: AAAI 2018, computing methodologies, machine learning, learning
paradigms, supervised learning, structured output
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