scholarly journals Towards a folksonomy graph-based context-aware recommender system of annotated books

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sara Qassimi ◽  
El Hassan Abdelwahed ◽  
Meriem Hafidi ◽  
Aimad Qazdar
Keyword(s):  
Recommender System ◽  
Contextual Information ◽  
Semantic Relatedness ◽  
Search Problem ◽  
Context Aware ◽  
Shared Resources ◽  
Real World Application ◽  
Collaborative Interactions ◽  
Partitioning Problem ◽  
Performance Results

AbstractThe emergence of collaborative interactions has empowered users by enabling their interactions through tagging practices that create a folksonomy, also called, classification of the shared resources, any identifiable thing or item on the system. In education, tagging is considered a powerful meta-cognitive strategy that successfully engages learners in the learning process. Besides, the collaborative tagging gathers learners’ opinions, thus, provides more comprehensible recommendations. Still, the abundant shared contents are mostly unorganized which makes it hard for users to select and discover the appropriate items of their interests. Thus, the use of recommender systems overcomes the distressing search problem by assisting users in their searching and exploring experience, and suggesting relevant items matching their preferences. In this regard, this article presents a folksonomy graphs based context-aware recommender system (CARS) of annotated books. The generated graphs express the semantic relatedness between these resources, i.e. books, by effectively modeling the folksonomy relationship between user-resource-tag and integrating contextual information within a multi-layer graph referring to a Knowledge Graph (KG). To put our proposal into shape, we model a real-world application of Goodbooks-10k dataset to recommend books. The proposed approach incorporates spectral clustering to deal with the graph partitioning problem. The experimental evaluation shows relevant performance results of graph-based book recommendations.

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

2021 ◽  
Author(s):  
Zainab Al-Zanbouri
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Web Services ◽  
Recommender System ◽  
Energy Efficient ◽  
Contextual Information ◽  
Three Dimensions ◽  
Context Aware ◽  
Rating Data ◽  
Recommendation Accuracy

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

A Hybrid Context Aware Recommender System with Combined Pre and Post-Filter Approach

2019 ◽  
Vol 10 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Mugdha Sharma ◽  
Laxmi Ahuja ◽  
Vinay Kumar
Keyword(s):  
Performance Improvement ◽  
Recommender System ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Experimental Setup ◽  
Context Aware ◽  
Contextual Modeling ◽  
Filter Approach ◽  
Post Filter

The domain of context aware recommender approaches has made substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored. There are generally three algorithms which can be used to include context and those are: pre-filter approach, post-filter approach, and contextual modeling. Each of the algorithms has their own drawbacks. The proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to user. With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach.

Review Aware Recommender System

2018 ◽  
Vol 10 (2) ◽  
pp. 28-50
Author(s):  
Fatima Zahra Lahlou ◽  
Houda Benbrahim ◽  
Ismail Kassou
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
Contextual Information ◽  
Empirical Evaluation ◽  
Feature Engineering ◽  
Context Aware ◽  
Recommendation Accuracy

Context aware recommender systems (CARS) are recommender systems (RS) that provide recommendations according to user contexts. The first challenge for building such a system is to get the contextual information. Some works tried to get this information from reviews provided by users in addition to their ratings. However, all of these works perform important feature engineering in order to infer the context. In this article, the authors present a new CARS architecture that allows to automatically use contextual information from reviews without requiring any feature engineering. Moreover, they develop a new CARS algorithm that is tailored to textual contexts, that they call Textual Context Aware Factorization Machines (TCAFM). An empirical evaluation shows that the proposed architecture allows to significantly improve recommendation accuracy using state of the art RS and CARS algorithms, whereas TCAFM leads to additional improvements.

scholarly journals Deep Learning-Based Context-Aware Recommender System Considering Contextual Features

2021 ◽  
Vol 12 (1) ◽  
pp. 45
Author(s):  
Soo-Yeon Jeong ◽  
Young-Kuk Kim
Keyword(s):  
Deep Learning ◽  
Recommender System ◽  
Contextual Information ◽  
User Preferences ◽  
Context Aware ◽  
Data Sparsity ◽  
Contextual Features ◽  
Proposed Model ◽  
Multidimensional Matrix ◽  
Sparsity Problem

A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users. However, as a user preferences matrix is expanded in a multidimensional matrix, data sparsity is maximized. In this paper, we propose a deep learning-based context-aware recommender system that considers the contextual features. Based on existing deep learning models, we combine a neural network and autoencoder to extract characteristics and predict scores in the process of restoring input data. The newly proposed model is able to easily reflect various type of contextual information and predicts user preferences by considering the feature of user, item and context. The experimental results confirm that the proposed method is mostly superior to the existing method in all datasets. Also, for the dataset with data sparsity problem, it was confirmed that the performance of the proposed method is higher than that of existing methods. The proposed method has higher precision by 0.01–0.05 than other recommender systems in a dataset with many context dimensions. And it showed good performance with a high precision of 0.03 to 0.09 in a small dimensional dataset.

scholarly journals Towards a Semantic Graph-based Recommender System. A Case Study of Cultural Heritage

2021 ◽  
Vol 27 (7) ◽  
pp. 714-733
Author(s):  
Sara Qassimi ◽  
El Hassan Abdelwahed
Keyword(s):  
Cultural Heritage ◽  
Recommender System ◽  
Information Overload ◽  
Semantic Relatedness ◽  
Collaborative Tagging ◽  
Shared Resources ◽  
Semantic Graph ◽  
System A ◽  
The Social ◽  
Heritage Area

Research on digital cultural heritage has raised the importance of providing visitors with relevant assistance before and during their visits. With the advent of the social web, the cultural heritage area is affected by the problem of information overload. Indeed, a large number of available resources have emerged coming from the social information systems (SocIS). Therefore, visitors are swamped with enormous choices in their visited cities. SocIS platforms use the features of collaborative tagging, named folksonomy, to commonly contribute to the management of the shared resources. However, collaborative tagging uses uncontrolled vocabulary which semanti- cally weakens the description of resources, consequently decreases their classification, clustering, thereby their recommendation. Therefore, the shared resources have to be pertinently described to ameliorate their recommendations. In this paper, we aim to enhance the cultural heritage visits by suggesting semantically related places that are most likely to interest a visitor. Our proposed approach represents a semantic graph-based recommender system of cultural heritage places through two steps; (1) constructing an emergent semantic description that semantically augments the place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach.

scholarly journals A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

2021 ◽  
Author(s):  
Zainab Al-Zanbouri
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Web Services ◽  
Recommender System ◽  
Energy Efficient ◽  
Contextual Information ◽  
Three Dimensions ◽  
Context Aware ◽  
Rating Data ◽  
Recommendation Accuracy

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

scholarly journals A CONTEXT-AWARE TOURISM RECOMMENDER SYSTEM BASED ON A SPREADING ACTIVATION METHOD

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 333-339 ◽  
Author(s):  
Z. Bahramian ◽  
R. Ali Abbaspour ◽  
C. Claramunt
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Contextual Information ◽  
User Profile ◽  
User Preferences ◽  
Activation Technique ◽  
Spreading Activation ◽  
Context Aware ◽  
The City

Users planning a trip to a given destination often search for the most appropriate points of interest location, this being a non-straightforward task as the range of information available is very large and not very well structured. The research presented by this paper introduces a context-aware tourism recommender system that overcomes the information overload problem by providing personalized recommendations based on the user’s preferences. It also incorporates contextual information to improve the recommendation process. As previous context-aware tourism recommender systems suffer from a lack of formal definition to represent contextual information and user’s preferences, the proposed system is enhanced using an ontology approach. We also apply a spreading activation technique to contextualize user preferences and learn the user profile dynamically according to the user’s feedback. The proposed method assigns more effect in the spreading process for nodes which their preference values are assigned directly by the user. The results show the overall performance of the proposed context-aware tourism recommender systems by an experimental application to the city of Tehran.

A Context-Aware Personalized Hybrid Book Recommender System

2020 ◽  
Author(s):  
Hossein Arabi ◽  
Vimala Balakrishnan ◽  
Nor Liyana Mohd Shuib
Keyword(s):  
Personality Traits ◽  
Recommender System ◽  
Contextual Information ◽  
Personality Inventory ◽  
Context Aware ◽  
User Characteristics ◽  
Improve Product ◽  
Product Features ◽  
Recommendation Accuracy ◽  
Filtering Techniques

Contextual information such as emotion, location and time can effectively improve product or service recommendations, however, studies incorporating them are lacking. This paper presents a context-aware recommender system, personalized based on several user characteristics and product features. The recommender system which was customized to recommend books, was aptly named as a Context-Aware Personalized Hybrid Book Recommender System, which utilized users’ personality traits, demographic details, location, review sentiments and purchase reasons to generate personalized recommendations. Users’ personality traits were determined using the Ten Item Personality Inventory. The results show an improved recommendation accuracy compared to the existing algorithms, and thus indicating that the integration of several filtering techniques along with specific contextual information greatly improves recommendations.

scholarly journals Web crawling based context aware recommender system using optimized deep recurrent neural network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Venugopal Boppana ◽  
P. Sandhya
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recommender System ◽  
Recommendation System ◽  
Contextual Information ◽  
Recommendation Systems ◽  
User Preferences ◽  
Bald Eagle ◽  
Context Aware ◽  
Deep Recurrent Neural Network

AbstractRecommendation systems are obtaining more attention in various application fields especially e-commerce, social networks and tourism etc. The top items are recommended based on the ability of recommender system which predict the future preference out of the available items. Because of the internet, the people in the current society has too many options that’s why the recommendation system is very essential. The recommendation is achieved by the particular users who predict the ratings for numerous items and recommend those items to other users. Majorly, content and collaborative filtering techniques are employed in typical recommendation systems to find user preferences and provide final recommendations. But, these systems commonly lacks to take growing user preferences in various contextual factors. Context aware recommendation systems consider various contextual parameters into account and attempt to catch user preferences appropriately. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. Therefore, in this paper an effective deep learning based context aware recommendation model is proposed which can be act as an efficient recommender system by showing minimum error during recommendation. Initially, the dataset is pre-processed using Natural Language Tool Kit (NLTK) in Python platform. After pre-processing, the TF–IDF and word embedding model is used for every pre-processed reviews to extract the features and contextual information. The extracted feature is considered as an input of density based clustering to group the negative, neutral and positive sentiments of user reviews. Finally, deep recurrent neural Network (DRNN) is employed to get the most preferable user from every cluster. The recurrent neural network model parameter values are initialized through the fitness computation of Bald Eagle Search (BES) algorithm. The proposed model is implemented using NYC Restaurant Rich Dataset using Python programming platform and performance is evaluated based on the metrics of accuracy, precision, recall and compared with existing models. The proposed recommendation model achieves 99.6% accuracy which is comparatively higher than other machine learning models.

Sign in / Sign up

Export Citation Format

Share Document