6,373 research outputs found
A conversational collaborative filtering approach to recommendation
Recent work has shown the value of treating recommendation as a conversation between user and system, which conversational recommenders have done by allowing feedback like ânot as expensive as thisâ on recommendations. This allows a more natural alternative to content-based information access. Our research focuses on creating a viable conversational methodology for collaborative-filtering recommendation which can apply to any kind of information, especially visual. Since collaborative filtering does not have an intrinsic understanding of the items it suggests, i.e. it doesnât understand the content, it has no obvious mechanism for conversation. Here we develop a means by which a recommender driven purely by collaborative filtering can sustain a conversation with a user and in our evaluation we show that it enables finding multimedia items that the user wants without requiring domain knowledge
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
Social contextuality and conversational recommender systems
As people continue to become more involved in both creating and consuming information, new interactive methods of retrieval are being developed. In this thesis we examine conversational approaches to recommendation, that is, the act of suggesting items to users based on the systemâĂĂ´s understanding of them. Conversational recommendation is a recent contribution to the task of information discovery. We propose a novel approach to conversation around recommendation, examining how it is improved to work with collaborative filtering, a common recommendation algorithm. In developing new ways to recommend information to people we also examine their methods of information seeking, exploring the role of conversational recommendation, using both interview and sensed brain signals.
We also look at the implications of the wealth of social and sensed information now available and how it improves the task of accurate recommendation. By allowing systems to better understand the connections between users and how their social impact can be tracked we show improved recommendation accuracy. We look at the social information around recommendations, proposing a directed influence approach between socially connected individuals, for the purpose of weighting recommendations with the wisdom of influencers. We then look at the semantic relationships that might seem to indicate wisdom (i.e. authors on a book-ranking site) to see if the ``wisdom of the few'' can be traced back to those conventionally considered wise in the area. Finally we look at ``contextuality'' (the ability of sets of contextual sensors to accurately recommend items across groups of people) in recommendation, showing that different users have very different uses for context within recommendation.
This thesis shows that conversational recommendation can be generalised to work well with collaborative filtering, that social influence contributes to recommendation accuracy, and that contextual factors should not be treated the same for each user
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
The benefits of opening recommendation to human interaction
This paper describes work in progress that uses an interactive recommendation process to construct new objects which are tailored to user preferences. The novelty in our work is moving from the recommendation of static objects like consumer goods, movies or books, towards dynamically-constructed recommendations which are built as part of the recommendation process. As a proof-of-concept we build running or jogging routes for visitors to a city, recommending routes to users according to their preferences and we present details of this system
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
Collaborative trails in e-learning environments
This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas â experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future
Deriving item features relevance from collaborative domain knowledge
An Item based recommender system works by computing a similarity between
items, which can exploit past user interactions (collaborative filtering) or
item features (content based filtering). Collaborative algorithms have been
proven to achieve better recommendation quality then content based algorithms
in a variety of scenarios, being more effective in modeling user behaviour.
However, they can not be applied when items have no interactions at all, i.e.
cold start items. Content based algorithms, which are applicable to cold start
items, often require a lot of feature engineering in order to generate useful
recommendations. This issue is specifically relevant as the content descriptors
become large and heterogeneous. The focus of this paper is on how to use a
collaborative models domain-specific knowledge to build a wrapper feature
weighting method which embeds collaborative knowledge in a content based
algorithm. We present a comparative study for different state of the art
algorithms and present a more general model. This machine learning approach to
feature weighting shows promising results and high flexibility
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