18,487 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
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
Capturing the Visitor Profile for a Personalized Mobile Museum Experience: an Indirect Approach
An increasing number of museums and cultural institutions
around the world use personalized, mostly mobile, museum
guides to enhance visitor experiences. However since a typical
museum visit may last a few minutes and visitors might only visit
once, the personalization processes need to be quick and efficient,
ensuring the engagement of the visitor. In this paper we
investigate the use of indirect profiling methods through a visitor
quiz, in order to provide the visitor with specific museum content.
Building on our experience of a first study aimed at the design,
implementation and user testing of a short quiz version at the
Acropolis Museum, a second parallel study was devised. This
paper introduces this research, which collected and analyzed data
from two environments: the Acropolis Museum and social media
(i.e. Facebook). Key profiling issues are identified, results are
presented, and guidelines towards a generalized approach for the
profiling needs of cultural institutions are discussed
Close Reading Oblivion: Character Believability and Intelligent Personalization in Games
This paper investigates issues of character believability and intelligent personalization through a reading of the Elder Scrolls: Oblivion. Oblivion’s opening sequence simultaneously trains players in the function of the game, and allows them to customize their character class through the choices and actions they take.
Oblivion makes an ambitious attempt at intelligent personalization in the character creation process. Its strategy is to track early gameplay decisions and “stereotype” players into one of 21 possible classes. This approach has two advantages over a less adaptive system. First, it supports the illusion of the game world as a real world by embedding the process of character creation within a narrativised game-play context. Second, the intelligent recommendation system responds to the player’s desire to believe that the game “knows” something about her personality. This leads the players to conceptualize the system as an entity with autonomous, humanlike knowledge.
Through the analysis of multiple replayings of the opening sequence, this paper considers ways in which Oblivion both succeeds and fails at mapping player behaviour to appropriate class assignments. The paper documents places where the dialogue between player and game breaks down, and argues for alternative techniques to customize the play experience within the desires of the player
Numerical and Evolutionary Optimization 2020
This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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