2,971 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
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
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
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
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
User effort vs. accuracy in rating-based elicitation
One of the unresolved issues when designing a recommender system is the number of ratings -- i.e., the profile length -- that should be collected from a new user before providing recommendations. A design tension exists, induced by two conflicting requirements. On the one hand, the system must collect "enough"ratings from the user in order to learn her/his preferences and improve the accuracy of recommendations. On the other hand, gathering more ratings adds a burden on the user, which may negatively affect the user experience. Our research investigates the effects of profile length from both a subjective (user-centric) point of view and an objective (accuracy-based) perspective. We carried on an offline simulation with three algorithms, and a set of online experiments involving overall 960 users and four recommender algorithms, to measure which of the two contrasting forces influenced by the number of collected ratings -- recommendations relevance and burden of the rating process -- has stronger effects on the perceived quality of the user experience. Moreover, our study identifies the potentially optimal profile length for an explicit, rating based, and human controlled elicitation strategy
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