Proposed Recommendation Model Research Articles

An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem

An e-learning recommender system (RS) aims to generate personalized recommendations based on learner preferences and goals. The existing RSs in the e-learning domain still exhibit drawbacks due to its inability to consider the learner characteristics in the recommendation process. In this paper, we are dealing with the new user cold-start problem, which is a major drawback in e-learning content RSs. This problem can be mitigated by incorporating additional learner data in the recommendation process. This paper proposes an ontology-based (OB) content recommender system for addressing the new user cold-start problem. In the proposed recommendation model, ontology is used to model the learner and learning objects with their characteristics. Collaborative and content-based filtering techniques are used in the recommendation model to generate the top N recommendations based on learner ratings. Experiments were conducted to evaluate the performance and prediction accuracy of the proposed model in cold-start conditions using the evaluation metrics mean absolute error, precision and recall. The proposed model provides more reliable and personalized recommendations by making use of ontological domain knowledge.

ICT-based Learner-Centric Evolutionary Learning Model: An effective solution to Teaching-Learning process

In India around in the second week of March 2020, in order to avoid spread of COVID-19 virus, the Central Governments have declared LOCKDOWN in the country. It results in temporary shutdown of schools and colleges. By looking around, at severity of situation due to spread of the novel corona virus, there is no certainty when schools and colleges will reopen. This is a crucial time for the education field, including board tests, kindergarten admissions, entry tests of various universities and competitive exams. It has greater impact on Education field. It has affected continuous teaching-learning process, student counselling operation, assessment methods of teachers, students’ interest towards higher studies in abroad got lower. Information Communication Technology (ICT) may play important role in the lockdown period by providing online learning platforms. Its need in education is discussed in this paper. With the help of power supply, basic digital skills to teachers and students and internet connectivity digital learning can be made possible. Similarly, students those are from low-income groups or presence of disability, etc. for them distance learning program can be included. To mitigate such hazardous situations, ICT-based learning model is essential. In this paper, ICT-based Learner-Centric Blended Education Model is proposed which blends traditional classroom teaching with e-learning where students and teachers both are active, students can interact with teachers; also, teachers can monitor progress of students, etc. After surveying different research papers on Education Models, it has been observed that in learning based on ICT, the Learner-centric environment need to be implemented. In online platform instructors provide course material to students but personalization of these materials is missing. The proposed work presented an ICT-based Learner-Centric Evolutionary Learning Model which suggests appropriate course material to learner, according to learner’s profile built by our profile builder module which includes details like learning style, demographic details, recommendations, expectations, likes and dislikes. Our proposed work presented is seamless integration of traditional teaching-learning methods with online teaching-learning methods. The equal importance to teachers is given here to design and recommend study material to different types of learners, their preference is at highest priority in proposed recommendation model. Active engagement of students in learning process increases education graph using technologies and finally it will be helpful in development of our country.

Mapping of learning style with learning object metadata for addressing cold-start problem in e-learning recommender systems

In the e-learning domain, content recommender systems had evolved to recommend relevant learning contents based on the learner preferences. One of the significant drawbacks of content recommenders in the e-learning domain is the new user cold-start problem. The objective of this study is to propose a recommendation model for addressing the cold-start problem using learner's learning style alone. Learning style refers to the way a learner prefers to learn and it is a prominent learner characteristic to understand the learner profile. In this study, we propose an ontology-based recommendation algorithm that makes use of the learning dimensions of the Felder Silverman Learning Style Model to map with the learning object characteristics. The knowledge about the learner and the learning objects are represented using ontology. Experiments were conducted to evaluate the accuracy of the proposed recommendation model using the evaluation metric and f-measure. The learner satisfaction with the proposed model is measured based on the ratings given to the learning objects by the participants of the experiment.

Preference and Constraint Factor Model for Event Recommendation

Newly emerging Event-based Social Network (EBSN) concentrates on connecting both online social relationships and offline local events. For the growing amount of events published on EBSNs, personalized event recommendation becomes essential to help users choose attractive events. But most of existing event recommendation algorithms fail to distinguish constraint factors of users’ event participation behaviors from preference factors, which reflects the cost of event participation that hinders users from attending interested events. To take full advantage of influences from contextual information on users’ event participation, we differentiate preference and constraint factors which contribute to users’ decision for event participation, and extract the soft spatial and temporal constraints from event venue and start time contexts respectively. Then we propose the Preference and Constraint Factor Model (PCFM) based on factorization machine model, using attentive mechanism to weight feature interactions and incorporate latent factors of users and contextual features for personalized perference modeling and event recommendation. Moreover, learning-to-rank techniques are utilized to train PCFM as a ranking model for the implicit feedback nature of responses from users. Extensive experiments evaluate the performance of our proposed recommendation model on real-world EBSN datasets, and demonstrate the outperformance than state-of-art event recommendation methods on many metrics.

A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based are representative solutions in recommender systems, however, the content-based model has some shortcomings, such as single kind of recommendation results, lack of effective perception of user preferences; while, for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this article, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

A Recommender System for Metaheuristic Algorithms for Continuous Optimization Based on Deep Recurrent Neural Networks

As revealed by the no free lunch theorem, no single algorithm can outperform any others on all classes of optimization problems. To tackle this issue, methods for recommending an existing algorithm for solving given problems have been proposed. However, existing recommendation methods for continuous optimization suffer from low practicability and transferability, mainly due to the difficulty in extracting features that can effectively describe the problem structure and lack of data for training a recommendation model. This work proposes a generic recommender system to address the above two challenges. First, a novel method is proposed to represent an analytic objective function of a continuous optimization problem as a tree, which is directly used as the features of the problem. For black-box optimization problems whose objective function is unknown, a symbolic regressor is adopted to estimate the tree structure. Second, a large number of benchmark problems are randomly created based on the proposed tree representation, providing an abundant amount of training data with various levels of difficulty. By employing a deep recurrent neural network, a recommendation model is trained to recommend a most suitable metaheuristic algorithm for white- or black-box optimization, making a significant step forward towards fully automated algorithm recommendation for continuous optimization. Experimental results on 100 000 benchmark problems show that the proposed recommendation model achieves considerably better performance than existing ones, and exhibits high transferability to real-world problems.

Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices

The electric vehicle (EV) industry has developed rapidly in the past decade, and the number of EVs has surged. When large scale EVs are connected to the power grid, the peak load of the power grid surges during the rush time of EV charging. The uncoordinated large scale of EV charging is harmful to the operation of the power system. Hence, dynamic pricing mechanisms have been proposed that use cost reduction as the motivation for EV owners to alter certain charging decisions. In this study, two optimal scheduling models for the charging of EVs, basic scheduling and recommendation models, are proposed to achieve cost minimization for EV owners in response to dynamic pricing. The basic scheduling model schedules the charging and discharging behaviours of EVs based on the connection and disconnection times of the EVs to the power grid. The costs involved in this model include the charging cost, discharging reward, degradation cost of EV battery, and parking fee. Based on the basic scheduling model, the proposed recommendation model recommends that the EV owner leave earlier to save on the parking fee, which may inconvenience the EV owner. This inconvenience is further considered in the costs, by measuring the coefficient of inconvenience to represent the time sensitivity of the EV owner. Finally, the comparison results of charging and discharging behaviours and the corresponding costs based on the two optimal scheduling models are sent to the EV owners. This can help EV owners, who have different degrees of sensitivity to time, to make optimal charging decisions. Moreover, the recommendation model can improve the utilization efficiency of charging facilities and relieve the pressure on them in the scheduling process when EVs are connected to the power grid on a large scale.

Fuzzy Near Compactness Based Personalized Recommendation for Preventing Patients with Type 2 Diabetes Mellitus and Hypertension from Cardiovascular Complication

Undeniably, preventing cardiovascular complication for patients with type 2 diabetes mellitus and hypertension is a challenge in the healthcare area. However, the prevention of these patients from a cardiovascular complication does not obtain much attention despite the rate of morbidity and mortality with cardiovascular complication increasing every year. To prevent each individual patient from not getting worse in health status, personalized recommendation is revealed as the most effective motivation in self-healthcare management with optimal process. Consequently, this paper proposes a personalized recommendation using the intelligent supporting system to prevent the patients with type 2 diabetes mellitus and hypertension from a cardiovascular complication, whereas the appropriated activities of daily living will be recommended as a suitable lifestyle. This recommendation system is modeled with fuzzy near compactness to measure the similarity between the patient’s conditions and practice plans in order to retrieve the most optimal practice programme which is obtained as a personalized plan for each patient. To conduct this model, this paper acquired the clinical data, lifestyle data, and trends of health status data from the patient history. The recommendation by this proposed model was suggested from the fuzzy near compactness algorithm which was constructed from the knowledge and experience of ten medical experts. The experimental results, on 744 datasets based on the total of checking up of 162 patients, show that the proposed recommendation model can achieve 96.63% accuracy when compared with the diagnostic results made by other five medical experts.

A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression

Recommender systems are common in e-commerce platforms in recent years. Recommender systems are able to help users find preferential items among a large amount of products so that users’ time is saved and sellers’ profits are increased. Cross-domain recommender systems aim to recommend items based on users’ different tastes across domains. While recommender systems usually suffer from the user cold-start problem that leads to unsatisfying recommendation performance, cross-domain recommendation can remedy such a problem. This article proposes a novel cross-domain recommendation model based on regression analysis, partial least squares regression (PLSR). The proposed recommendation models, PLSR-CrossRec and PLSR-Latent, are able to purely use source-domain ratings to predict the ratings for cold-start users who never rated items in the target domains. Experiments conducted on the Epinions dataset with ten various domains’ rating records demonstrate that PLSR-Latent can outperform several matrix factorization-based competing methods under a variety of cross-domain settings. The time efficiency of PLSR-Latent is also satisfactory.

Recommendation using Rule based Implicative Rating Measure

The paper presents a rule based implicative rating measure to calculate the ratings of users on items. The paper also presents a new model using the ruleset with the rule length of 2 and the proposed measure to suggest to users the list of items with the highest ratings. The new model is compared to the three existing models that use items (such as the popular items, the items with highest similarities, and the items with strong relationships) to make the suggestion. The experiments on the MSWeb dataset and the MovieLens dataset indicate that the proposed recommendation model has the higher performace (via the Precision - Recall and the ROC curves) than the compared models for most of the given.

A novel task recommendation model for mobile crowdsourcing systems

With the developments of sensors in mobile devices, mobile crowdsourcing systems are attracting more and more attention. However, how to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowdsourcing systems. This paper proposes a novel task recommendation model for mobile crowdsourcing systems. Considering both user similarity and task similarity, the recommendation probabilities of tasks are derived. Based on dwell-time, the latent recommendation probability of tasks can be predicted. In addition, the trust of tasks is obtained based on their reputations and participation frequencies. Finally, we perform comprehensive experiments towards the Amazon metadata and YOOCHOOSE datasets to verify the effectiveness of the proposed recommendation model.

A graph-based model to improve social trust and influence for social recommendation

Social big data is large scale of data due to exponential popularity of social network and social media. Researchers can use social big data and social network for their observations if they analyse those in an intelligent manner. The target of intelligent decision is to find the most credible user in social network, who has the highest influence. A very large number of users are connected in social networks, implicitly friends-of-friends or explicitly mutual friends. They are able to communicate with each other and share their likes or dislikes on different topics. If users want to analyse any topic or purchase product like movie, book, they are populated with a lot of choices. Information overload due to large number of choices available to users limits effective product selection and hence results in reduced users’ satisfaction. Recommendation models are solution for providing better suggestion to users. Product’s recommendation at Amazon, friend’s recommendation at Facebook and music recommendation at iTunes are some of the popular examples of suggestions made on the basis of user’s interests. Recommendation models ease the user by reducing search space in social network graph. The main purpose of this paper is to improve social recommendations so that better and more appropriate choices are available for users. In this paper, an efficient technique for social recommendations using hyperedge and transitive closure is proposed. Social big data is processed and analysed in the form of social graphs. User–user and user–item connections are represented in the form of matrices. We have exploited homophily so that large number of connected users have trust on each other. Our model provides better recommendation to users by leveraging increased trust. The proposed model overcomes the limitations of traditional recommender systems like sparsity, cold start. It also improves prediction accuracy. The proposed model is evaluated through different metrics like MAE, precision, recall and RMSE. Empirical analysis shows significant improvement in recommendations. We have used Mahout library for improving recommendation accuracy and also handling large volume of data. SNAP library is also used for analysis of social big graphs. The proposed recommendation model is evaluated using Epinions and FilmTrust datasets. These datasets contain user’s ratings for various products in the scale of 1–5. Through analysis it is verified that the proposed model boosts the performance significantly. We have formulated recommendation model using manipulated social graph as per our proposed technique. This manipulated graph is mentioned as influence product graph (IPG) throughout this paper. IPG increases social trust value between connected users and this effect in recommending products in an effective and efficient manner.

Exploiting organizer influence and geographical preference for new event recommendation

AbstractThe rapid growth of event‐based social networks (EBSNs) has generated great demand for personalized event recommendation. Indeed, new events are published every day in EBSNs. In this paper, we focus on the problem of recommending new events (i.e., the newly published events that have not yet received any user response) to registered users in EBSNs. Notice that collaborative filtering is ineffective for recommending new events due to the short lifetime of events and the lack of historical information, that is, the cold‐start problem. A straightforward approach to address this problem is to adopt content‐based recommendation techniques, for example, deriving user interests through the content information of the events they attended. Nevertheless, we observe that organizer influence and geographical preference also play important roles in a user's decision to participate in an event. Motivated by this observation, we combine user interest, organizer influence, and geographical preference to present a unified model for new event recommendation. In particular, we utilize a topic model to derive user interests while adopting matrix factorization to infer organizer influences on users from the interactions between users and organizers. Moreover, we model users' geographical preferences via their location histories of attended events. We conduct a performance evaluation using real‐world data collected from DoubanEvent. The experimental results demonstrate the effectiveness of our proposed recommendation model.

DMFA-SR: Deeper Membership and Friendship Awareness for Social Recommendation

The existing social recommendation models mostly utilize various explicit user-generated information. Although there exist a few studies adopting the implicit relationship between users for social recommendation, however, these studies do not consider the deeper social relationship, nor simultaneously take into account two or more deeper relationships between users from different angles. To this end, we propose a new deeper membership and friendship awareness for social recommendation. Specifically, we first calculate the deeper membership similarity between users utilizing the improved Jaccard similarity coefficient and the deeper friendship similarity between users using the proposed two-hop random walk algorithm. Second, the deeper membership similarity and the deeper friendship similarity are combined in a unified way to form a comprehensive deeper social relation similarity. Third, we adopt the matrix factorization method incorporating the deeper membership and the deeper friendship between users as a regularization term for social recommendation, and the corresponding comprehensive deeper social relationship similarity is regarded as the regularization parameter. Experiments on two real-world datasets demonstrate the superiority of the proposed recommendation model.

A Novel Task Recommendation Model for Mobile Crowdsourcing Systems

With the developments of sensors in mobile devices, mobile crowdsourcing systems are attracting more and more attention. However, how to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowdsourcing systems. This paper proposes a novel task recommendation model for mobile crowdsourcing systems. Considering both user similarity and task similarity, the recommendation probabilities of tasks are derived. Based on dwell-time, the latent recommendation probability of tasks can be predicted. In addition, the trust of tasks is obtained based on their reputations and participation frequencies. Finally, we perform comprehensive experiments towards the Amazon metadata and YOOCHOOSE datasets to verify the effectiveness of the proposed recommendation model.

A Hybrid Service Recommendation Prototype Adapted for the UCWW: A Smart-City Orientation

With the development of ubiquitous computing, recommendation systems have become essential tools in assisting users in discovering services they would find interesting. This process is highly dynamic with an increasing number of services, distributed over networks, bringing the problems of cold start and sparsity for service recommendation to a new level. To alleviate these problems, this paper proposes a hybrid service recommendation prototype utilizing user and item side information, which naturally constitute a heterogeneous information network (HIN) for use in the emerging ubiquitous consumer wireless world (UCWW) wireless communication environment that offers a consumer-centric and network-independent service operation model and allows the accomplishment of a broad range of smart-city scenarios, aiming at providing consumers with the “best” service instances that match their dynamic, contextualized, and personalized requirements and expectations. A layered architecture for the proposed prototype is described. Two recommendation models defined at both global and personalized level are proposed, with model learning based on the Bayesian Personalized Ranking (BPR). A subset of the Yelp dataset is utilized to simulate UCWW data and evaluate the proposed models. Empirical studies show that the proposed recommendation models outperform several widely deployed recommendation approaches.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

A trust-based probabilistic recommendation model for social networks

In social networks, how to establish an effective recommendation model is an important research topic. This paper proposes a trust-based probabilistic recommendation model for social networks. We consider the recommendation attributes of products to determine similarity among users. Then inherent similarity among products is taken into account to derive the transition probability of a target node. In addition, trust of products is obtained based on their reputations and purchase frequencies. In order to solve the problem of users׳ cold start, we consider users׳ latent factor to find their latent similar users. Finally, we adopt the Amazon product co-purchasing network metadata to verify the effectiveness of the proposed recommendation model through comprehensive experiments. Furthermore, we analyze the impact of the transition probability influence factor through experiments. The experimental results show that the proposed recommendation model is effective and has a higher accuracy.

A probabilistic model to resolve diversity–accuracy challenge of recommendation systems

Recommendation systems have wide-spread applications in both academia and industry. Traditionally, performance of recommendation systems has been measured by their precision. By introducing novelty and diversity as key qualities in recommender systems, recently increasing attention has been focused on this topic. Precision and novelty of recommendation are not in the same direction, and practical systems should make a trade-off between these two quantities. Thus, it is an important feature of a recommender system to make it possible to adjust diversity and accuracy of the recommendations by tuning the model. In this paper, we introduce a probabilistic structure to resolve the diversity–accuracy dilemma in recommender systems. We propose a hybrid model with adjustable level of diversity and precision such that one can perform this by tuning a single parameter. The proposed recommendation model consists of two models: one for maximization of the accuracy and the other one for specification of the recommendation list to tastes of users. Our experiments on two real datasets show the functionality of the model in resolving accuracy–diversity dilemma and outperformance of the model over other classic models. The proposed method could be extensively applied to real commercial systems due to its low computational complexity and significant performance.