Context-aware Recommender Systems Research Articles

MCARS-CC: A Salable Multicontext-Aware Recommender System

Context-aware recommendation systems (CARSs) leverage contextual information, e.g., time, location, or mood, to generate more personalized recommendations with high accuracy; however, existing CARSs fall short in: 1) handling the high sparsity of data; 2) designing scalable solutions in real time; and 3) providing more personalized solutions with the current limited static contexts. This article proposes a multi-CARS based on consensus clustering (MCARS-CC) to solve these challenges. The item-based contextual information is acquired using explicit static and inferred contexts by applying sentiment analysis to the users’ reviews. The proposed model is experimented using contextual prefiltering and postfiltering techniques applied to two benchmark datasets, Yelp and TripAdvisor. The model is evaluated using mean absolute error (MAE), root-mean-squared error (RMSE), response time, precision, recall, and F-measure. The experimental results show that the proposed MCARS-CC model outperforms other baseline techniques using the accuracy and error-based metrics. Incorporating hypergraph partitioning algorithm (HGPA) could improve the MAE and RMSE by 25.96% and 8.94% (Yelp), respectively. Also, HGPA led to an 18.47% and 15.94% improvement ratio in terms of MAE and RMSE (TripAdvisor), respectively.

Dynamic Context-Aware Recommender System for Home Automation Through Synergistic Unsupervised and Supervised Learning Algorithms

Dynamic Context-Aware Recommender System for Home Automation Through Synergistic Unsupervised and Supervised Learning Algorithms

DyCARS: A dynamic context-aware recommendation system.

Dynamic recommendation systems aim to achieve real-time updates and dynamic migration of user interests, primarily utilizing user-item interaction sequences with timestamps to capture the dynamic changes in user interests and item attributes. Recent research has mainly centered on two aspects. First, it involves modeling the dynamic interaction relationships between users and items using dynamic graphs. Second, it focuses on mining their long-term and short-term interaction patterns. This is achieved through the joint learning of static and dynamic embeddings for both users and items. Although most existing methods have achieved some success in modeling the historical interaction sequences between users and items, there is still room for improvement, particularly in terms of modeling the long-term dependency structures of dynamic interaction histories and extracting the most relevant delayed interaction patterns. To address this issue, we proposed a Dynamic Context-Aware Recommendation System for dynamic recommendation. Specifically, our model is built on a dynamic graph and utilizes the static embeddings of recent user-item interactions as dynamic context. Additionally, we constructed a Gated Multi-Layer Perceptron encoder to capture the long-term dependency structure in the dynamic interaction history and extract high-level features. Then, we introduced an Attention Pooling network to learn similarity scores between high-level features in the user-item dynamic interaction history. By calculating bidirectional attention weights, we extracted the most relevant delayed interaction patterns from the historical sequence to predict the dynamic embeddings of users and items. Additionally, we proposed a loss function called the Pairwise Cosine Similarity loss for dynamic recommendation to jointly optimize the static and dynamic embeddings of two types of nodes. Finally, extensive experiments on two real-world datasets, LastFM, and the Global Terrorism Database showed that our model achieves consistent improvements over state-of-the-art baselines.

Guiding Evacuees to Improve Fire Building Evacuation Efficiency: Hazard and Congestion Models to Support Decision Making by a Context-Aware Recommender System

Fires in large buildings can have tragic consequences, including the loss of human lives. Despite the advancements in building construction and fire safety technologies, the unpredictable nature of fires, particularly in large buildings, remains an enormous challenge. Acknowledging the paramount importance of prioritising human safety, the academic community has been focusing consistently on enhancing the efficiency of building evacuation. While previous studies have integrated evacuation simulation models, aiding in aspects such as the design of evacuation routes and emergency signalling, modelling human behaviour during a fire emergency remains challenging due to cognitive complexities. Moreover, behavioural differences from country to country add another layer of complexity, hindering the creation of a universal behaviour model. Instead of centring on modelling the occupant behaviour, this paper proposes an innovative approach aimed at enhancing the occupants’ behaviour predictability by providing real-time information to the occupants regarding the most suitable evacuation routes. The proposed models use a building’s environmental conditions to generate contextual information, aiding in developing solutions to make the occupants’ behaviour more predictable by providing them with real-time information on the most appropriate and efficient evacuation routes at each moment, guiding the occupants to safety during a fire emergency. The models were incorporated into a context-aware recommender system for testing purposes. The simulation results indicate that such a system, coupled with hazard and congestion models, positively influences the occupants’ behaviour, fostering faster adaptation to the environmental conditions and ultimately enhancing the efficiency of building evacuations.

Context aware Markov chains models

For more than a century, Markov chain models have had a tremendous effect on research and industry alike in various domains such as physics, chemistry, biology, and computer science. However, limited research has been performed on how to incorporate contextual conditions into the modeling phase. Existing approaches are performed in an ad hoc manner or fail to consider multiple contextual dimensions simultaneously. Inspired by the main paradigms in context-aware recommender systems, we suggest five novel approaches for learning contextual Markov chain models. In particular we suggest three contextual pre-filtering models that operate by learning multiple sub-models from various data partitions, each tailored to specific contextual aspects; a contextual model that directly extents the Markovian model with the contextual features; and lastly, a post-filtering model, which serves to refine the predictions made by a traditional non-contextual model by considering the available contextual information. We evaluate the suggested methods in two use cases: analysis of web browsing activities, and attack propagation patterns in honeypot systems. Experiments with massive datasets that contain millions of records indicate that taking the context of modeled sequences into consideration can substantially improve the accuracy of predicting and ranking the next possible element in such sequences. Moreover, we demonstrate the high scalability of the suggested methods, making them suitable for analyzing a vast amount of sequential data.

Large Language Models as Recommendation Systems in Museums

This paper proposes the utilization of large language models as recommendation systems for museum visitors. Since the aforementioned models lack the notion of context, they cannot work with temporal information that is often present in recommendations for cultural environments (e.g., special exhibitions or events). In this respect, the current work aims to enhance the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations that are aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-aware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.

Analysis of Final Ratings in Tourism Using Various Machine Learning Techniques

Abstract: User-generated ratings are a tremendous asset to product descriptions and significantly impact decision-making in Context-Aware Recommender Systems. Researchers exploit this information to predict user preferences, model the item's attributes, and offer intelligible recommendations. However, not all contextual ratings are significant because they may be posted by various users for various reasons and based on different routines. Further, as users care about different attributes of multiple contexts, not all user ratings equally reflect the users' opinion of the overall rating, a primary concern in recommender systems. This article predicts the overall rating using user-user and item collaborative filtering with significant contexts and the outcome tested on various machine and deep learning models using the contextual segments.

Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective

Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner’s current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context-aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. During the last decade, several approaches have emerged in the literature to define the learning context and the factors that may capture it. Those approaches led to different definitions of contextualized learner-profiles. In this paper, we review the state-of-the-art approaches for defining a user’s learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view. By combining those two viewpoints, we aim to bridge a gap between both domains, in terms of contextualizing learning recommendations.

FGCR: Fused graph context-aware recommender system

Context-aware recommender systems (CARS), which capture user preferences by incorporating user interactions and side information, have attracted widespread attention in today’s information age. With the emergence of graph convolution networks (GCN) in recent years, graph methods have been shown effective in establishing global connections between users, items, and contexts. In this paper, we first introduce a unified graph recommendation framework to incorporate both GCN and traditional models, showing the similarities and differences between them in terms of parameter configurations and message passing styles. Then, based on the empirical analysis of existing models, we propose a novel Fused Graph Context-aware Recommender system (FGCR) model to address the identified limitations. FGCR fuses the parameter configuration of both item-based models and GCN, modeling robust node relationships in user-item-context interaction graph through a sparse item correlation matrix and dense node embeddings. In addition, FGCR applies a novel masked graph convolution strategy to refine the information aggregation process with various types of nodes. Experiments conducted on five real-world datasets show that FGCR significantly improves performance compared to the seven baseline models. Ablation study and user group analysis validate the effectiveness of each component in FGCR, especially in modeling active users and sparse contextual information.

Context-aware recommender system for multi-user smart home

<span lang="EN-US">Smart home is one of the most important applications of the internet of things (IoT). Smart home makes life simpler, easier to control, saves energy based on user’s behavior and interaction with the home appliances. Many existing approaches have designed a smart home system using data mining algorithms. However, these approaches do not consider multiusers that exist in the same location and time (which needs a complex control). They also use centralized mining algorithm, then the system’s efficiency is reduced when datasets increase. Therefore, in this paper, we firstly build a context-aware recommender system that considers multi-user’s preferences and solves their conflicts by using unsupervised algorithms to deliver useful recommendation services. Secondly, we improve smart home’s responsive using parallel computing. The results reveal that the proposed method is better than existing approaches.</span>

Competitive gamification in crowdsourcing-based contextual-aware recommender systems

During the COVID-19 outbreak, crowdsourcing-based context-aware recommender systems (CARS) which capture the real-time context in a contactless manner played an important role in the “new normal”. This study investigates whether this approach effectively supports users’ decisions during epidemics and how different game designs affect users performing crowdsourcing tasks. This study developed a crowdsourcing-based CARS focusing on restaurant recommendations. We used four conditions (control, self-competitive, social-competitive, and mixed gamification) and conducted a two-week field study involving 68 users. The system provided recommendations based on real-time contexts including restaurants’ epidemic status, allowing users to identify suitable restaurants to visit during COVID-19. The result demonstrates the feasibility of crowdsourcing to collect real-time information for recommendations during COVID-19 and reveals that a mixed competitive game design encourages both high- and low-performance users to engage more and that a game design with self-competitive elements motivates users to take on a wider variety of tasks. These findings inform the design of restaurant recommender systems in an epidemic context and serve as a comparison of incentive mechanisms for gamification of self-competition and competition with others.

Deep Learning-Based Context-Aware Recommender System Considering Change in Preference

In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to predict preferences by considering the user’s context. A context-aware recommender system uses contextual information such as time, weather, and location to predict preferences. However, a user’s preferences are not always the same in a given context. They may follow trends or make different choices due to changes in their personal environment. Therefore, in this paper, we propose a context-aware recommender system that considers the change in users’ preferences over time. The proposed method is a context-aware recommender system that uses Matrix Factorization with a preference transition matrix to capture and reflect the changes in users’ preferences. To evaluate the performance of the proposed method, we compared the performance with the traditional recommender system, context-aware recommender system, and dynamic recommender system, and confirmed that the performance of the proposed method is better than the existing methods.

DCARS: Deep context-aware recommendation system based on session latent context

Recommendation systems (RSs) usually create suggestions based on users’ prior intentions. Users’ interests may evolve due to context change or user-mode change. Discovering such a change is crucial for producing personalized suggestions. Traditional approaches assume that each user has a fixed preference. On the contrary, context-aware recommendation systems (CARSs) use contextual information to detect user intention changes. However, applying contextual information is the main challenge in CARSs, because it is not always feasible to achieve all the users’ contextual information. Furthermore, adding different contexts to RSs grows its dimensionality in multiple applications. Besides, existing CARSs cannot precisely obtain the hierarchical relationships between items and contexts items that influence users’ intentions. They often use short-term interest with either static long-term preference in the recommendation process. To alleviate the mentioned challenges, we propose a novel deep context-aware recommendation system (DCARS) to capture and incorporate user preferences changes in the recommendation process. The proposed method models the latent context among selected items in each session throughout users’ historical interactions and combines users’ short-term and long-term preferences to generate recommendations. Specifically, we suggest a DCARS based on latent representations of sessions derived from users’ activities. The experiment results on benchmark context-aware data sets show that the proposed DCARS model surpasses state-of-the-art approaches.

Research on multi-context aware recommendation methods based on tensor factorization

Compared to the traditional recommender systems, context-aware recommender systems are more in line with actual application contexts. However, the existing researches are mostly focused on single context-aware recommendation, such as time-aware recommendation or location-aware recommendation, and lack of in-depth research on multi-context-aware recommendation. Therefore, we proposed a recommendation method of high-order tensor factorization based on multi-context-aware. First, on the basis of analyzing the influence of context on users’ interest preferences, the sensitivity of users to multiple contexts was detected using statistical methods. For context-sensitive users, four-dimensional tensors and feature matrices used to solve data sparsity were constructed based on rating matrix and situational information. And then the stochastic gradient descent algorithm was used for iterative calculation to fill in missing data values and carry out parameter optimization. For context-insensitive users, we used matrix factorization to predict users’ interest preferences. Finally, we tested and validated our method on a multi-context-aware movie dataset, and the experimental results show that the proposed method could effectively reduce the prediction error and improve the recommendation quality.

Dynamic context management in context-aware recommender systems

Context-aware recommendation is an essential part of advanced advertising systems. Most of the existing context-aware recommender systems (CARS) build recommendation models considering context as a static entity. In practice, user preferences are dynamic and change over time. In this study, we argue that CARS must be able to dynamically opt for the evolving behavior of contextual attributes. We propose a solution with a runtime identification of contextual information and jointly integrate content analysis to boost recommendation performance. Initially, the proposed model constructs a list of contextually similar candidates. The users’ context information extracted in the initial step is modeled together with the content information to estimate a context score used to find contextually reliable neighbors. Instead of relying on a fixed rating matrix, we dynamically exploit contextual information for effective rating prediction. Experiments on three real-world datasets demonstrate that our method outperforms classical and state-of-the-art recommendation algorithms.

Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation. The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users. However, the key issue is how contextual information is used to create good and intelligent recommender systems. This paper proposes an Artificial Neural Network (ANN) to achieve contextual recommendations based on user-generated reviews. The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS. Thus, the most appropriate contexts in which a user should choose an item or service are achieved. This work converts every label set into a Multi-Label Classification (MLC) problem to enhance recommendations. Experimental results show that the proposed ANN performs better in the Binary Relevance (BR) Instance-Based Classifier, the BR Decision Tree, and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset. Furthermore, the accuracy of the proposed ANN achieves better results by 1.1% to 6.1% compared to other existing methods.

A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems

Recommender systems are similar to an information filtering system that helps identify items that best satisfy the users’ demands based on their preference profiles. Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems. CARSs have integrated additional contextual information such as time, place, and so on for providing better recommendations. However, the majority of CARSs use ratings as a unique criterion for building communities. Meanwhile, MCRSs utilize user preferences in multiple criteria to better generate recommendations. Up to now, how to exploit context in MCRSs is still an open issue. This paper proposes a novel approach, which relies on deep learning for context-aware multi-criteria recommender systems. We apply deep neural network (DNN) models to predict the context-aware multi-criteria ratings and learn the aggregation function. We conduct experiments to evaluate the effect of this approach on the real-world dataset. A significant result is that our method outperforms other state-of-the-art methods for recommendation effectiveness.

Distributed Focused Web Crawling for Context Aware Recommender System using Machine Learning and Text Mining Algorithms

In today’s world, Recommender System (RS) is the most effective means used to manage the huge amount of multimedia content available on the internet. RS learns the user preferences and relationships among the users and items. It helps the users to discover new interesting items and make use of different media types such as text, audio, video and images. RS can act as an information filtering model which can overcome the issues related to over-fitting and excess information. In this work, a new distributed framework named DAE-SR (Deep AutoEncoder based Softmax Regression) is introduced for context-aware recommender systems which focus on user-item based interaction and offers personalized recommendations. The proposed model is implemented in PYTHON platform. The dataset used for experimentation is Foursquare dataset. The performance of the proposed context-aware RS is beneficial to both the users and service providers. Its helps in decision making process and can offer relevant recommendations to users. The performance is evaluated in terms of various metrics such as accuracy, recall, precision and so on. From the implementation outcomes, the proposed strategy achieved good accuracy (98.33%), precision (98%), run time (1.43 ms) and recall (98.1%). Thus, it is proved that the proposed DAE-SR classifier performs better compared to other models and offer dependable and relevant recommendations to users.

How do context-aware artificial intelligence algorithms used in fitness recommender systems? A literature review and research agenda

Recommender Systems (RS) help the user in the decision-making process when there is a problem of plenty or lack of information. The context-aware recommender systems (CARS) incorporate contextual information to improve the efficiency of RS. Prior works on CARS have compared algorithms and reported application domains. However, the practical challenges involved in CARS implementation with technological advances have not been adequately examined. We chose fitness as the application domain as there is increased user adoption, and the complexity is high. It involves the dynamic interplay of multi-dimensional contexts and technological advances in contextual attributes and incorporates machine learning algorithms in the process of context detection. We conduct textual analysis and conduct a systematic review to identify developments. The concept map demonstrates the components of contextuality, the methods adopted, and the role of AI techniques in amplifying contextuality in the algorithm. The paper identifies the challenges and presents the research opportunities.

Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review

The practice of quantified-self sleep tracking has become increasingly common among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization and are incapable of providing actionable recommendations that are tailored to users’ physical, behavioral, and environmental context. A promising solution to address this gap is the context-aware sleep health recommender system (CASHRS), an emerging research field that bridges ubiquitous sleep computing and context-aware recommender systems. This paper presents a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges identified in peer-reviewed publications that meet the characteristics of CASHRS. The analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS.