153 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
Assessing and improving recommender systems to deal with user cold-start problem
Recommender systems are in our everyday life. The recommendation methods have as
main purpose to predict preferences for new items based on userŠs past preferences. The
research related to this topic seeks among other things to discuss user cold-start problem,
which is the challenge of recommending to users with few or no preferences records.
One way to address cold-start issues is to infer the missing data relying on side information.
Side information of different types has been explored in researches. Some
studies use social information combined with usersŠ preferences, others user click behavior,
location-based information, userŠs visual perception, contextual information, etc. The
typical approach is to use side information to build one prediction model for each cold
user. Due to the inherent complexity of this prediction process, for full cold-start user in
particular, the performance of most recommender systems falls a great deal. We, rather,
propose that cold users are best served by models already built in system.
In this thesis we propose 4 approaches to deal with user cold-start problem using
existing models available for analysis in the recommender systems. We cover the follow
aspects:
o Embedding social information into traditional recommender systems: We investigate
the role of several social metrics on pairwise preference recommendations and
provide the Ąrst steps towards a general framework to incorporate social information
in traditional approaches.
o Improving recommendation with visual perception similarities: We extract networks
connecting users with similar visual perception and use them to come up with
prediction models that maximize the information gained from cold users.
o Analyzing the beneĄts of general framework to incorporate networked information
into recommender systems: Representing different types of side information as a
user network, we investigated how to incorporate networked information into recommender
systems to understand the beneĄts of it in the context of cold user
recommendation.
o Analyzing the impact of prediction model selection for cold users: The last proposal
consider that without side information the system will recommend to cold users
based on the switch of models already built in system.
We evaluated the proposed approaches in terms of prediction quality and ranking
quality in real-world datasets under different recommendation domains. The experiments
showed that our approaches achieve better results than the comparison methods.Tese (Doutorado)Sistemas de recomendação fazem parte do nosso dia-a-dia. Os métodos usados nesses
sistemas tem como objetivo principal predizer as preferências por novos itens baseado no
perĄl do usuário. As pesquisas relacionadas a esse tópico procuram entre outras coisas
tratar o problema do cold-start do usuário, que é o desaĄo de recomendar itens para
usuários que possuem poucos ou nenhum registro de preferências no sistema.
Uma forma de tratar o cold-start do usuário é buscar inferir as preferências dos usuários
a partir de informações adicionais. Dessa forma, informações adicionais de diferentes tipos
podem ser exploradas nas pesquisas. Alguns estudos usam informação social combinada
com preferências dos usuários, outros se baseiam nos clicks ao navegar por sites Web,
informação de localização geográĄca, percepção visual, informação de contexto, etc. A
abordagem típica desses sistemas é usar informação adicional para construir um modelo
de predição para cada usuário. Além desse processo ser mais complexo, para usuários
full cold-start (sem preferências identiĄcadas pelo sistema) em particular, a maioria dos
sistemas de recomendação apresentam um baixo desempenho. O trabalho aqui apresentado,
por outro lado, propõe que novos usuários receberão recomendações mais acuradas
de modelos de predição que já existem no sistema.
Nesta tese foram propostas 4 abordagens para lidar com o problema de cold-start
do usuário usando modelos existentes nos sistemas de recomendação. As abordagens
apresentadas trataram os seguintes aspectos:
o Inclusão de informação social em sistemas de recomendação tradicional: foram investigados
os papéis de várias métricas sociais em um sistema de recomendação de
preferências pairwise fornecendo subsidíos para a deĄnição de um framework geral
para incluir informação social em abordagens tradicionais.
o Uso de similaridade por percepção visual: usando a similaridade por percepção
visual foram inferidas redes, conectando usuários similares, para serem usadas na
seleção de modelos de predição para novos usuários.
o Análise dos benefícios de um framework geral para incluir informação de redes
de usuários em sistemas de recomendação: representando diferentes tipos de informação
adicional como uma rede de usuários, foi investigado como as redes de
usuários podem ser incluídas nos sistemas de recomendação de maneira a beneĄciar
a recomendação para usuários cold-start.
o Análise do impacto da seleção de modelos de predição para usuários cold-start:
a última abordagem proposta considerou que sem a informação adicional o sistema
poderia recomendar para novos usuários fazendo a troca entre os modelos já
existentes no sistema e procurando aprender qual seria o mais adequado para a
recomendação.
As abordagens propostas foram avaliadas em termos da qualidade da predição e da
qualidade do ranking em banco de dados reais e de diferentes domínios. Os resultados
obtidos demonstraram que as abordagens propostas atingiram melhores resultados que os
métodos do estado da arte
A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation
International audienceHow can we effectively recommend items to a user about whom we have no information? This is the problem we focus on, known as the cold-start problem. In this paper, we focus on the cold user problem.In most existing works, the cold-start problem is handled through the use of many kinds of information available about the user. However, what happens if we do not have any information?Recommender systems usually keep a substantial amount of prediction models that are available for analysis. Moreover, recommendations to new users yield uncertain returns. Assuming a number of alternative prediction models is available to select items to recommend to a cold user, this paper introduces a multi-armed bandit based model selection, named PdMS.In comparison with two baselines, PdMS improves the performance as measured by the nDCG.These improvements are demonstrated on real, public datasets
Interactive social recommendation
National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ
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