317 research outputs found
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
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
A Study on User Demographic Inference Via Ratings in Recommender Systems
Everyday, millions of people interact with online services that adopt recommender systems, such as personalized movie, news and product recommendation services. Research has shown that the demographic attributes of users such as age and gender can further improve the performance of recommender systems and can be very useful for many other applications such as marketing and social studies. However, users do not always provide those details in their online profiles due to privacy concern. On the other hand, user interactions such as ratings in recommender systems may provide an alternative way to infer demographic information. Most existing approaches can infer user demographics based on sufficient interaction history but could fail for users with few ratings. In this thesis, we study the association between users demographic information and their ratings, and explore the tradeoff between user privacy and the utility of personalization. In particular, we present a novel multi-task preference elicitation method, with which a recommender system asks a new user to rate selected items adaptively and infers the demographics rapidly via a few interactions. Experimental results on real-world datasets demonstrate the performance of the proposed method in terms of the accuracy of both demographics inference and rating prediction
Novel Methods Using Human Emotion and Visual Features for Recommending Movies
Postponed access: the file will be accessible after 2022-06-01This master thesis investigates novel methods using human emotion as contextual information to estimate and elicit ratings when watching movie trailers. The aim is to acquire user preferences without the intrusive and time-consuming behavior of Explicit Feedback strategies, and generate quality recommendations. The proposed preference-elicitation technique is implemented as an Emotion-based Filtering technique (EF) to generate recommendations, and is evaluated against two other recommendation techniques. One Visual-based Filtering technique, using low-level visual features of movies, and one Collaborative Filtering (CF) using explicit ratings. In terms of \textit{Accuracy}, we found the Emotion-based Filtering technique (EF) to perform better than the two other filtering techniques. In terms of \textit{Diversity}, the Visual-based Filtering (VF) performed best. We further analyse the obtained data to see if movie genres tend to induce specific emotions, and the potential correlation between emotional responses of users and visual features of movie trailers. When investigating emotional responses, we found that \textit{joy} and \textit{disgust} tend to be more prominent in movie genres than other emotions. Our findings also suggest potential correlations on a per movie level. The proposed Visual-based Filtering technique can be adopted as an Implicit Feedback strategy to obtain user preferences. For future work, we will extend the experiment with more participants and build stronger affective profiles to be studied when recommending movies.Masteroppgave i informasjonsvitenskapINFO390MASV-INF
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
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
Alleviating the new user problem in collaborative filtering by exploiting personality information
The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-016-9172-zThe new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3-40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.This work was supported by the Spanish Ministry of Economy and
Competitiveness (TIN2013-47090-C3). We thank Michal Kosinski and David Stillwell for
their attention regarding the dataset
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Item cold-start is a classical issue in recommender systems that affects
anime and manga recommendations as well. This problem can be framed as follows:
how to predict whether a user will like a manga that received few ratings from
the community? Content-based techniques can alleviate this issue but require
extra information, that is usually expensive to gather. In this paper, we use a
deep learning technique, Illustration2Vec, to easily extract tag information
from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE
(Blended Alternate Least Squares with Explanation), a new model for
collaborative filtering, that benefits from this extra information to recommend
mangas. We show, using real data from an online manga recommender system called
Mangaki, that our model improves substantially the quality of recommendations,
especially for less-known manga, and is able to provide an interpretation of
the taste of the users.Comment: 6 pages, 3 figures, 1 table, accepted at the MANPU 2017 workshop,
co-located with ICDAR 2017 in Kyoto on November 10, 201
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