1,624 research outputs found
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
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
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