Context-aware Recommender Systems Research Articles

Restaurant recommender system based on sentiment analysis

Today, exploiting sentiment analysis has become popular in designing recommender systems in various fields, including the restaurant and food area. However, most of the sentiment analysis-based restaurant recommender systems only use static information such as food quality, price, and service quality. The analysis of users’ opinions and the extraction of their food preferences lead to the provision of personalized recommendations, which is a research gap in literature; In this paper, a context-aware recommender system is proposed that extracts the food preferences of individuals from their comments and suggests restaurants in accordance with these preferences. For this purpose, the semantic approach is used to cluster the name of foods extracted from users’ comments and analyze their sentiments about them. Finally, nearby open restaurants are recommended based on their similarity to user preferences. For evaluation, the TripAdvisor website has been used and comments from 100 different users have been collected during the first 9 months of 2018. The precision, recall and f-measure of the system are measured in three scenarios of top1, top3, and top5. The results indicate that the proposed system can provide recommendations with a precision of 92.8%, giving users a high degree of precision. Besides, the system outperforms the previous research in these criteria.

A State-of-the-Art Survey on Context-Aware Recommender Systems and Applications

In the digital transformation era, increasingly more individuals and organizations use or create services in digital spaces. Many business transactions have been moving from the offline to online mode. For example, sellers intend to introduce their products on e-commerce platforms rather than display them on store shelves as in traditional business. Although this new format business has advantages, such as more space for product displays, more efficient searches for a specific item, and providing a good tool for both buyers and sellers to manage their products, it is also accompanied by the obviously important problem that users are confused when choosing an appropriate item due to a large amount of information. For this reason, the need for a recommendation system appears. Informally, a recommender system is similar to an information filtering system that helps identify a set of items that best satisfy users' demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions, and user's mood) into recommender systems to improve their performance has recently received considerable attention in the research literature. However, incorporating such contextual information into recommendation models is a challenging task because of the increase in both the dimensionality and sparsity of the model. Different approaches with their own advantages and disadvantages have been proposed. This paper provides a comprehensive survey on context-aware recommender systems in recent years. In particular, the authors pay more attention to journal and conference proceedings papers published from 2016 to 2020. In addition, this paper also presents open issues for context-aware recommender systems and discuss promising directions for future research.

Context-Aware Recommender Systems in the Music Domain: A Systematic Literature Review

The design of recommendation algorithms aware of the user’s context has been the subject of great interest in the scientific community, especially in the music domain where contextual factors have a significant impact on the recommendations. In this type of system, the user’s contextual information can come from different sources such as the specific time of day, the user’s physical activity, and geolocation, among many others. This context information is generally obtained by electronic devices used by the user to listen to music such as smartphones and other secondary devices such as wearables and Internet of Things (IoT) devices. The objective of this paper is to present a systematic literature review to analyze recent work to date in the field of context-aware recommender systems and specifically in the domain of music recommendation. This paper aims to analyze and classify the type of contextual information, the electronic devices used to collect it, the main outstanding challenges and the possible opportunities for future research directions.

An Analysis of learners’ affective and cognitive traits in Context-Aware Recommender Systems (CARS) using feature interactions and Factorization Machines (FMs)

Massive Open Online Courses (MOOCs) have witnessed a fast emergence in recent years thanks to their open and massive nature. A huge number of learners with different profiles, knowledge and learning objectives follow the same online courses with different personalized experiences. With this rapid development, course recommendation based on traits and characteristics becomes a necessity during the learning process to determine potentially suitable learning objects while analyzing user’s behaviours. Context-Aware Recommender Systems (CARS) have been shown to be effective in recommending items according to user’s interests and affect. In this paper, we propose a novel approach for Context-Aware Recommender Systems based on Factorization Machines (FMs). We propose a new algorithm called Unsupervised Graph Predictor Factorization Machines (UGPFMs) that models feature interactions during the recommendation process. UGPFMs apply Convolutional Neural Networks (CNNs) in the graph predictor to model interactions between context features in the rating matrice, then it employs the Factorization Machine (FM) to select the suitable items for the recommendation. UGPFM is a generic model that extends FM to improve the accuracy of recommendation, it outperforms state-of-the-art feature interactions-based FM techniques using MSE, RMSE, MAE and R-Squared metrics. We applied UGPFMs on MOOC while considering the sentiment, the cognition and the confusion as contexts for the recommendation. We compared different context combinations’ impact on the recommendation accuracy, then we computed each context-to-context feature interactions to better understand each context efficiency on e-learning recommendation.

MORec: At the crossroads of context-aware and multi-criteria decision making for online music recommendation

Context-aware recommender systems have received considerable attention from industry and academic areas. In this paper, we pay heed to the growing interest in integrating context-awareness and multi-criteria decision making in recommender systems, to deal with the most pressing challenges in music recommender systems, namely the diversity of the recommended playlist, the scalability of the system, and the cold start problem. This paper introduces a new multi-criteria recommendation approach, named MORec, which generates Top-N music recommendations by bootstrapping the system using beforehand collected data. We usher by gauging the relevance of contextual information from the relation between three elements: user, music genre, and the user’s context. Then, we apply an aggregation technique to uncover the relationship between the context and the overall rating. Besides, we apply the K-means algorithm to generate a predictive model that comprises clusters of similar contexts defining the association between contextual dimensions and music genres. Carried out experiments emphasize very promising results of our approach in terms of clustering quality, compared to the Partitioning Around Medoids algorithm in terms of connectivity and stability. The comparison versus pioneering recommendation baselines underscored the effectiveness of MORec in terms of recommendation quality and usefulness.

How to select and weight context dimensions conditions for context-aware recommendation?

Contextual information plays a key role in Context-Aware Recommender Systems (CARS). The rating prediction in CARS focuses on improving recommendation accuracy attempting to form a personalized information recommendation for users. Three key problems that affect the performances of recommender systems: (i) context condition’s selection; (ii) context condition’s weighting; and (iii) users’ context conditions matching. Context-aware approaches have the assumption that all context conditions have the same weight. These approaches ignore that users have different preferences in different contexts. To address these three problems, we introduce a novel approach for Selecting, Weighting Context Conditions (SWCC) and measuring semantic similarity between users’ situations. Evaluation experiments show that the proposed approach is outperforming the pioneering context-aware recommendation approaches of the literature.

Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems

Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems

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

The 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.

Quantum context-aware recommendation systems based on tensor singular value decomposition

In this paper, we propose a quantum algorithm for recommendation systems which incorporates the contextual information of users to the personalized recommendation. The preference information of users is encoded in a third-order tensor of dimension N which can be approximated by the truncated tensor singular value decomposition (t-svd) of the subsample tensor. Unlike the classical algorithm that reconstructs the approximated preference tensor using truncated t-svd, our quantum algorithm obtains the recommended product under certain context by measuring the output quantum state corresponding to an approximation of a user’s dynamic preferences. The algorithm achieves the time complexity \(\mathcal {O}(\sqrt{k}N\mathrm{polylog}(N))\), compared to the classical counterpart with complexity \(\mathcal {O}(kN^3)\), where k is the truncated tubal rank.

A Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems

Abstract Privacy protection is one of the key concerns of users in recommender system-based consumer markets. Popular recommendation frameworks such as collaborative filtering (CF) suffer from several privacy issues. Federated learning has emerged as an optimistic approach for collaborative and privacy-preserved learning. Users in a federated learning environment train a local model on a self-maintained item log and collaboratively train a global model by exchanging model parameters instead of personalized preferences. In this research, we proposed a federated learning-based privacy-preserving CF model for context-aware recommender systems that work with a user-defined collaboration protocol to ensure users’ privacy. Instead of crawling users’ personal information into a central server, the whole data are divided into two disjoint parts, i.e. user data and sharable item information. The inbuilt power of federated architecture ensures the users’ privacy concerns while providing considerably accurate recommendations. We evaluated the performance of the proposed algorithm with two publicly available datasets through both the prediction and ranking perspectives. Despite the federated cost and lack of open collaboration, the overall performance achieved through the proposed technique is comparable with popular recommendation models and satisfactory while providing significant privacy guarantees.

Enhancing Top-N Recommendation Using Stacked Autoencoder in Context-Aware Recommender System

Context-aware recommender systems (CARS) are a vital module of many corporate, especially within the online commerce domain, where consumers are provided with recommendations about products potentially relevant for them. A traditional CARS, which utilizes deep learning models considers that user’s preferences can be predicted by ratings, reviews, demographics, etc. However, the feedback given by the users is often conflicting when comparing the rating score and the sentiment behind the reviews. Therefore, a model that utilizes either ratings or reviews for predicting items for top-N recommendation may generate unsatisfactory recommendations in many cases. In order to address this problem, this paper proposes an effective context-specific sentiment based stacked autoencoder (CSSAE) to learn the concrete preference of the user by merging the rating and reviews for a context-specific item into a stacked autoencoder. Hence, the user's preferences are consistently predicted to enhance the Top-N recommendation quality, by adapting the recommended list to the exact context where an active user is operating. Experiments on four Amazon (5-core) datasets demonstrated that the proposed CSSAE model persistently outperforms state-of-the-art top-N recommendation methods on various effectiveness metrics.

A Review of Content-Based and Context-Based Recommendation Systems

In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

Trust and Distrust based Cross-domain Recommender System

ABSTRACT A recommender system (RS) provides assistance for users to filter out items of their interest in the presence of millions of available items. The reason is to find out the likewise user with the assumption that if users have shared similar interest in the past then they may share the same in future. Collaborative filtering (CF) is the widely used recommendation algorithm due to its ease of use but suffers with the problems of sparsity and cold start problem. In this paper, we propose a trust and distrust-based cross domain context aware recommender system in the multi-agent environment which tries to reduce the problem of data sparsity in collaborative-filtering recommender system and improves coverage. Cross Domain Recommender System (CDRS) utilizes data from multiple domains to reduce the problem of sparsity. Moreover, the combination of trust and distrust in recommendation help to improve trustworthiness of generated recommendation. Distrust provides higher accuracy in recommendation by incorporating knowledge about the malicious users. Prototype of the system is developed using JADE and Java technology for the tourism domain consisting of restaurant, hotel, travel places and shopping places as sub-domains. The performance of the proposed trust and distrust-based cross domain recommender system is compared with the traditional approach of recommendation along with the cross domain approach and trust-based cross-domain approach in terms of accuracy and coverage. The results show that the proposed system outperforms in terms of both accuracy and coverage.

Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes.

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the “music mainstream” strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervized learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user’s country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-à-vis state-of-the-art algorithms that do not exploit this type of context information.

AI-based mobile context-aware recommender systems from an information management perspective: Progress and directions

In the Artificial Intelligence (AI) field, and particularly within the area of Machine Learning (ML), recommender systems have attracted significant research attention. These systems attempt to alleviate the increasing information overload that users can experience in the current Big Data era, by providing personalized recommendations of items that they may find relevant. Besides, given the importance of mobile computing, these systems have evolved to consider also the dynamic context of the mobile users (location, time, weather conditions, etc.) to offer them more appropriate suggestions and information while on the move.In this paper, we provide an extensive survey of recent advances towards intelligent mobile Context-Aware Recommender Systems (mobile CARS) from an information management perspective, with an emphasis on mobile computing and AI techniques, along with an analysis of existing research gaps and future research directions. We focus on approaches that go beyond just considering the location of the user and exploit also other context information. In this study, we have identified that deep learning approaches are promising artificial intelligence models for mobile CARS. Additionally, in a near future, we expect a higher prominence of push-based recommendation solutions where at least part of the recommendation engine could be executed in the mobile devices, which could share data and tasks in a distributed way.

Context-aware recommender system using trust network

Context-Aware Recommender Systems (CARS) improve traditional Recommender Systems (RS) in a wide array of domains and applications. However, CARS suffer from several inherent issues such as data sparsity and cold start. Incorporating trust into recommender systems can handle these issues. Trust-aware recommender systems use information from social networks such as trust statements, which prove another valuable information source. This paper exploits the advantages of these two systems by incorporating both trust and context information. We propose a hybrid approach: Trust based Context aware Post Filtering Approach that uses trust statements as a rich information with context compensation method of contextual post-filtering approach. Our approach utilizes the relative average difference among the context on output of trust aware collaborative filtering by incorporating explicit and implicit trust information. We also use a confidence concept to remove non-confident users from the trust network before generating prediction. The performed experiments show that the proposed approach improves upon the standard RS and outperforms recommendation approaches on real world dataset.

A novel context-aware recommender system based on a deep sequential learning approach (CReS)

With an increase in online longitudinal users’ interactions, capturing users’ precise preferences and giving accurate recommendations have become an urgent need for all businesses. Existing sequence-aware methods generally exploit a static low-rank vector for acquiring the overall sequential features, and incorporate context information as auxiliary input. As a result, they have a restricted modeling ability for extracting multi-grained sequential behaviors over contextual information. In other words, they poorly capture the hierarchical relationship between context relations and item relations that currently influence users’ preferences in a unified framework. Besides, they usually utilize users’ short-term preferences with either static or irrelevant long-term representation for the prediction. To tackle the above issues, in this paper, we propose a novel Context-aware Recommender System Based on a Deep Sequential Learning Approach (CReS) to capture users’ dynamic preferences by modeling the hierarchical relationships between contexts and items in a particular session, and for combining users’ short-term sessions with the relevant long-term representations. Specifically, within a certain session, we design a hierarchical attention network between the identified context relations and items relations, namely CReSession. Therefore, with CReSession, we could provide a suitable session representation that mimics the hierarchical user interests on multiple granularities of contextual types and its corresponding items. We then introduce a neural attentive bi-directional GRU network to distill only those highly related to the recent short-term session. Finally, the relevant long-term representations and the short-term session are combined with the sequential residual connection to form the final user representation in a unified manner. With extensive experiments on two real-world datasets, CReS not only achieves significant improvement over the state-of-the-art methods in terms of pre-defined metrics, but also provides an interpretable result regarding why we recommend these items to users.

Incorporating multidimensional information into dynamic recommendation process to cope with cold start and data sparsity problems

Area-of-Interest (AOI) recommendation is a type of context-aware recommendation that works based on location-based data. A context-aware recommender system should be able to provide the recommendations to the users based on their request time. Often, existing context-aware systems assume that the context information is constant over time and ignore dynamic users’ preferences. Besides, the development of a technique for incorporating auxiliary information within the recommendation models is a challenging task for the recommender systems. On another hand, the context pre/post filtering methods are inefficient in dealing with cold start and data sparsity problems. In these issues, there is not sufficient data to provide the accurate recommendation for the users. In this paper, a dynamic contextual modeling approach is proposed for improving the personalization in the sparse dataset. For this purpose, a density-based clustering algorithm is used for discovering the AOIs and coping with the sparsity problem. In addition, a hybrid similarity measurement is proposed to incorporate the auxiliary information into the recommendation process and suggest dynamic personalized AOIs in which the dynamic preferences of users are computed implicitly. The proposed similarity scheme using the auxiliary information can determine the neighborhood users for the cold start users, and accordingly, it can provide a list of recommendations to a target user. The experimental results based on Flickr and Yelp datasets demonstrate that the proposed method outperforms prior work on all three metrics, achieving a 10% increase on precision, a 25% increase on recall and 12% increase on F-Score in terms of quality metrics.

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. This has led to more research being done in this area stimulated by the omnipresence of smartphones and the latest web technologies. These systems are able to detect specific user needs and adapt recommendations to actual user context. In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. For this purpose, we used a systematic literature review methodology which clearly defined the scope, the objective, the timeframe, the methods, and the tools to undertake this research. Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this field. In order to have a clear vision of the research potential in the field, we considered research articles published between 2015 and 2020 and used a research portal giving access to major scientific research databases. Primary research articles selected are reviewed and the recommendation process is analyzed to identify the approach, the techniques, and the context elements employed in the development of the recommendation systems. The paper presents the detail of the review study, provides a synthesis of the results, proposes an evaluation based on measurable evaluation tools developed in this study, and advocates future research and development pathways in this interesting field.

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.