1,661 research outputs found
UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations
Recommender systems aim to enhance the overall user experience by providing
tailored recommendations for a variety of products and services. These systems
help users make more informed decisions, leading to greater user satisfaction
with the platform. However, the implementation of these systems largely depends
on the context, which can vary from recommending an item or package to a user
or a group. This requires careful exploration of several models during the
deployment, as there is no comprehensive and unified approach that deals with
recommendations at different levels. Furthermore, these individual models must
be closely attuned to their generated recommendations depending on the context
to prevent significant variation in their generated recommendations. In this
paper, we propose a novel unified recommendation framework that addresses all
four recommendation tasks, namely personalized, group, package, or
package-to-group recommendation, filling the gap in the current research
landscape. The proposed framework can be integrated with most of the
traditional matrix factorization-based collaborative filtering models. The idea
is to enhance the formulation of the existing approaches by incorporating
components focusing on the exploitation of the group and package latent
factors. These components also help in exploiting a rich latent representation
of the user/item by enforcing them to align closely with their corresponding
group/package representation. We consider two prominent CF techniques,
Regularized Matrix Factorization and Maximum Margin Matrix factorization, as
the baseline models and demonstrate their customization to various
recommendation tasks. Experiment results on two publicly available datasets are
reported, comparing them to other baseline approaches that consider individual
rating feedback for group or package recommendations.Comment: 25 page
A context aware recommender system for tourism with ambient intelligence
Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programming (CSP). It is proposed the architecture according to ambient intelligence perspective to allow the best possible tourist place to the end-user. The key development of this RS is representing the model in CSP and optimizing the problem. We implemented our system in Minizinc solver with domain restrictions represented by user preferences. The CSP allowed user preferences to guide the system toward finding the optimal solutions; RESUMO
O sistema de recomendação (RS) detém um lugar significativo na área do sector do turismo. O principal fator do planeamento de viagens é selecionar pontos de interesse relevantes (PoI) do domínio do turismo. O sistema de recomendação (SR) deve recolher informações de comportamentos, personalidade, preferências e outras informações contextuais do utilizador. Este trabalho centra-se principalmente na personalidade, preferências do utilizador e na análise de traços fisiológicos do utilizador. O trabalho tem como objetivo melhorar a modelação do perfil do utilizador, expondo a relação entre a personalidade deste e as categorias dos POI, assim como encontrar uma solução com programação por restrições (CSP). Propõe-se a arquitetura de acordo com a perspetiva do ambiente inteligente para conseguir o melhor lugar turístico possível para o utilizador final. A principal contribuição deste SR é representar o modelo como CSP e tratá-lo como problema de otimização. Implementámos o nosso sistema com o solucionador em Minizinc com restrições de domínio representadas pelas preferências dos utilizadores. O CSP permitiu que as preferências dos utilizadores guiassem o sistema para encontrar as soluções ideais
Using K-means Clustering and Similarity Measure to Deal with Missing Rating in Collaborative Filtering Recommendation Systems
The Collaborative Filtering recommendation systems have been developed to address the information overload problem and personalize the content to the users for business and organizations. However, the Collaborative Filtering approach has its limitation of data sparsity and online scalability problems which result in low recommendation quality. In this thesis, a novel Collaborative Filtering approach is introduced using clustering and similarity technologies. The proposed method using K-means clustering to partition the entire dataset reduces the time complexity and improves the online scalability as well as the data density. Moreover, the similarity comparison method predicts and fills up the missing value in sparsity dataset to enhance the data density which boosts the recommendation quality. This thesis uses MovieLens dataset to investigate the proposed method, which yields amazing experimental outcome on a large sparsity data set that has a higher quality with lower time complexity than the traditional Collaborative Filtering approaches
Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge
Conversational Recommender Systems (CRS) has become an emerging research
topic seeking to perform recommendations through interactive conversations,
which generally consist of generation and recommendation modules. Prior work on
CRS tends to incorporate more external and domain-specific knowledge like item
reviews to enhance performance. Despite the fact that the collection and
annotation of the external domain-specific information needs much human effort
and degenerates the generalizability, too much extra knowledge introduces more
difficulty to balance among them. Therefore, we propose to fully discover and
extract internal knowledge from the context. We capture both entity-level and
contextual-level representations to jointly model user preferences for the
recommendation, where a time-aware attention is designed to emphasize the
recently appeared items in entity-level representations. We further use the
pre-trained BART to initialize the generation module to alleviate the data
scarcity and enhance the context modeling. In addition to conducting
experiments on a popular dataset (ReDial), we also include a multi-domain
dataset (OpenDialKG) to show the effectiveness of our model. Experiments on
both datasets show that our model achieves better performance on most
evaluation metrics with less external knowledge and generalizes well to other
domains. Additional analyses on the recommendation and generation tasks
demonstrate the effectiveness of our model in different scenarios
Use of discrete choice models with recommender systems
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (leaves 130-133).Recommender systems, also known as personalization systems, are a popular technique for reducing information overload and finding items that are of interest to the user. Increasingly, people are turning to these systems to help them find the information that is most valuable to them. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. All of the known recommendation techniques have strengths and weaknesses, and many researchers have chosen to combine techniques in different ways. In this dissertation, we investigate the use of discrete choice models as a radically new technique for giving personalized recommendations. Discrete choice modeling allows the integration of item and user specific data as well as contextual information that may be crucial in some applications. By giving a general multidimensional model that depends on a range of inputs, discrete choice subsumes other techniques used in the literature. We present a software package that allows the adaptation of generalized discrete choice models to the recommendation task. Using a generalized framework that integrates recent advances and extensions of discrete choice allows the estimation of complex models that give a realistic representation of the behavior inherent in the choice process, and consequently a better understanding of behavior and improvements in predictions. Statistical learning, an important part of personalization, is realized using Bayesian procedures to update the model as more observations are collected.(cont.) As a test bed for investigating the effectiveness of this approach, we explore the application of discrete choice as a solution to the problem of recommending academic courses to students. The goal is to facilitate the course selection task by recommending subjects that would satisfy students' personal preferences and suit their abilities and interests. A generalized mixed logit model is used to analyze survey and course evaluation data. The resulting model identifies factors that make an academic subject "recommendable". It is used as the backbone for the recommender system application. The dissertation finally presents the software architecture of this system to highlight the software package's adaptability and extensibility to other applications.by Bassam H. Chaptini.Ph.D
Probabilistic Personalized Recommendation Models For Heterogeneous Social Data
Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks
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