Collaborative Filtering Techniques Research Articles

Course Recommendation System based on Natural Language Processing

Abstract—Computers are actually capable of understand hu- man languages thanks to the era referred to as NLP (Natural Language Processing). Deep grammatical and semantic analysis frequently makes use of words as the primary unit. The principle goal of NLP is regularly word segmentation. This undertaking can be used to deal with the sensible problem of widespread structural variations between conventional and multi-modal envi- ronments and diverse statistics modalities. The challenge presents the basics of the multi-modal function extraction approach that uses deep gaining knowledge of. A advice device primarily based on content material is also contributed by the undertaking. Based on the person’s previous behaviour or express feedback, it recommends additional items which might be just like what they already like the usage of object functions. A course recommenda-tion gadget, in a nutshell, is a device that suggests the subsequent piece of content based on what got already seen and loved. Advice structures are utilized by services like Spotify, Netflix, and Youtube to signify the subsequent movie or track you need to watch primarily based on what you’ve got already watched or heard. Primarily based at the filtering records, the advice gadget foresees gadgets which can realise and provide excessive- potential content that has been chosen by the consumer. Primarily based at the customers’ various searches, a recommendation machine become evolved to indicate courses to them. Users now have an easier time finding the proper guides based on their searches thanks to this machine. To determine availability, the system employs the TF-IDF algorithm and content material-based totally filtering. Suggestions for customers who use the collaborative filtering technique had been researched in earlier studies. Index Terms—Recommender System, Feature Extraction, Nat-ural Language Processing, Collaborative Filtering

Explaining the Performance of Collaborative Filtering Methods with Optimal Data Characteristics

The performance of a Collaborative Filtering (CF) method is based on the properties of a User-Item Rating Matrix (URM). And the properties or Rating Data Characteristics (RDC) of a URM are constantly changing. Recent studies significantly explained the variation in the performances of CF methods resulted due to the change in URM using six or more RDC. Here, we found that the significant proportion of variation in the performances of different CF techniques can be accounted to two RDC only. The two RDC are the number of ratings per user or Information per User (IpU) and the number of ratings per item or Information per Item (IpI). And the performances of CF algorithms are quadratic to IpU (or IpI) for a square URM. The findings of this study are based on seven well-established CF methods and three popular public recommender datasets: 1M MovieLens, 25M MovieLens, and Yahoo! Music Rating datasets.

A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems

Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations.

Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques.

Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique

The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process.

Web API Recommendation Based on Self-Attentional Neural Factorization Machines With Domain Interactions

With the advances of service computing technology, there has been a significant focus on enhancing the relevance or accuracy of Web API recommendation for Mashup creation. In the multiple established works, factorization machines based models portray increased performance compared with traditional content or collaborative filtering techniques with regard to accuracy, which still has great room for improvement. Thus, this article takes a further step to propose a more expressive factorization machines model, i.e., Self-Attentional Neural Factorization Machines with Domain Interactions for Web API recommendation. Specifically, we introduce the concept of domain interaction to reduce the calculation complexity and capture the common characteristics of all features in one domain. Then, we leverage an implicit attention mechanism, that is, self-attention, to identify the importance of different feature interactions, instead of using explicit attention. Moreover, to promote expressive capability, deep neural networks are integrated into the transformed feature interactions following the self-attention propagation, capturing low and high order representations. In this approach, the importance of feature interactions can contribute to both low and high order relations. Multiple studies on a practical dataset indicates the inherent benefits of the method proposed in this study over the baselines for Web API recommendation.

Retraction Note: Framework for social tag recommendation using Lion optimization algorithm and collaborative filtering techniques

Retraction Note: Framework for social tag recommendation using Lion optimization algorithm and collaborative filtering techniques

HybridRec: A recommender system for tagging GitHub repositories

Software repositories are increasingly essential to support the management of typical artifacts building up projects, including source code, documentation, and bug reports. GitHub is at the forefront of this kind of platforms, providing developer with a reservoir of code contained in more than 28M repositories. To help developers find the right artifacts, GitHub uses topics, which are short texts assigned to the stored artifacts. However, assigning inappropriate topics to a repository might hamper its popularity and reachability. In our previous work, we implemented MNBN and TopFilter to recommend GitHub topics. MNBN exploits a stochastic network to predict topics, while TopFilter relies on a syntactic-based function to recommend topics. In this paper, we extend our work by building HybridRec, a recommender system based on stochastic and collaborative-filtering techniques to generate more relevant topics. To deal with unbalanced datasets, we employ a Complement Naïve Bayesian Network (CNBN). Furthermore, we apply a preprocessing phase to clean and refine the input data before feeding the recommendation engine. An empirical evaluation demonstrates that HybridRec outperforms three state-of-the-art baselines, obtaining a better performance with respect to various metrics. We conclude that the conceived framework can be used to help developers increase their projects’ visibility.

Optimization Mold and Algorithm of Risk Control for Power Grid Corporations Based on Collaborative Filtering Technology.

With the ever-changing internal and external environmental factors of enterprises, various uncertainties and risks faced by enterprises are increasing, and the feasibility of financial meltdown is increasing. Research on financial meltdown early warning can help enterprises to prevent the occurrence of peril in advance and take resultful measures to ensure the healthy development of enterprises. If a serious financial meltdown leads to the bankruptcy of enterprises, the financial meltdown is not sudden, but a gradual process. The occurrence of financial meltdown is not only a harbinger, but also predictable. Therefore, it is an urgent question to be solved for listed corporations in China that how to mine the message with early warning function from a large amount of financial data generated in the business process of enterprises. The continuous maturity of data mining technique just solves this question. Based on collaborative filtering technique, this paper analyzes the risk control optimization mold and algorithm of power grid corporations, which is of great signification. After research, this algorithm is 30% better than the traditional algorithm, and it is suitable to be proverbially used.

Web Service QoS Prediction via Collaborative Filtering: A Survey

With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services. Collaborative filtering (CF), which is among the most successful personalized prediction techniques for recommender systems, has been widely applied to Web service QoS prediction. In addition to using conventional CF techniques, a number of studies extend the CF approach by incorporating additional information about services and users, such as location, time, and other contextual information from the service invocations. There are also some studies that address other challenges in QoS prediction, such as adaptability, credibility, privacy preservation, and so on. In this survey, we summarize and analyze the state-of-the-art CF QoS prediction approaches of Web services and discuss their features and differences. We also present several Web service QoS datasets that have been used as benchmarks for evaluating the predition accuracy and outline some possible future research directions.

Toward Preference and Context-Aware Hybrid Tourist Recommender System Based on Machine Learning Techniques

With the development of machine learning, to improve the accuracy in recommendation systems, the main purpose of the suggested approach consists of using techniques and algorithms which can predict and suggest relevant tourist services (k-items) to users according to their interests, needs, or tastes. In this research, we describe how machine learning techniques can automatically provide personalized recommendations to the requestors by considering both their preferences and their implicit/explicit contextual information and the current contextual constraints of the points of interest. We build an efficient, intelligent H-RN algorithm that hybridizes both the most known machine learning algorithms, namely Random Forest and Naïve Bayes, with both collaborative filtering techniques (model-based and memory-based technique). Different experiments of our approach as part of a recommender system in the touristic field are performed over the four large real-world datasets. Recommender systems can use H-RN to improve recommendation prediction and reduce the search space of tourist services. Moreover, the results of recall, precision, accuracy, F-measure, and average rate, as well as a set of statistical tests (One-way ANOVA, Diversity) and error metrics (RMSE, MAE) have been discussed to show the improvement of the prediction accuracy of our algorithm compared to the baseline approaches in various settings.

Improved Collaborative Filtering Recommender System Based on Missing Values Imputation on E-Commerce

One of the important aspects in e-commerce is how to recommend a product to users accurately. To achieve this goal, many e-commerce starts to build and research about recommender system. Many methods can be used to build a recommender system, one of them is using the collaborative filtering technique. This technique often experiences data sparsity problem that can impact to the recommender system prediction accuracy. To solve this problem, we apply improved collaborative filtering. This method predicts the missing values in the user item rating matrix. First, we do an initial selection to determine potential users who have the same characteristics with the active user. After that, we calculate the average distance between the active user and the other selected user. Next, we calculate missing values prediction. Missing values predictions is only done for items that have never been rated by other’s selected user but has been rated by the active user. We used Amazon electronic product with high sparsity level in this research to simulate the actual condition of e-commerce. We used MAE and RMSE to measure prediction accuracy. The methods we apply succeeds to improve the prediction accuracy compare to the conventional collaborative filtering method. The average MAE for method that we apply is 0.78 and RMSE 1.07

Feature-blind fairness in collaborative filtering recommender systems

Recommender systems were originally proposed for suggesting potentially relevant items to users, with the unique objective of providing accurate suggestions. These recommenders started being adopted in several domains, and were identified as generating biased results that could harm the data items being recommended. The exposure in generated rankings, for instance in a job candidate selection situation, is supposed to be fairly distributed among candidates, regardless of their sensitive attributes (gender, race, nationality, age) for promoting equal opportunities. It can happen, however, that no such sensitive information is available in the data applied for training the recommender, and in this case, there is still space for biases that can lead to unfair treatment, named Feature-Blind unfairness. In this work, we adopt Variational Autoencoders (VAE), considered as the state-of-the-art technique for Collaborative Filtering (CF) recommendations, and we present a framework for addressing fairness when having only access to information about user-item interactions. More specifically, we are interested in Position and Popularity Bias. VAE loss function combines two terms associated with accuracy and quality of representation; we introduce a new term for encouraging fairness, and demonstrate the effect of promoting fair results despite of a tolerable decrease in recommendation quality. In our best scenario, position bias is reduced by 42% despite a reduction of 26% in recall in the top 100 recommendation results, compared to the same situation without any fairness constraints.

Developing a Location-Based Recommender System Using Collaborative Filtering Technique in the Tourism Industry

The rapid growth of new information and products in the virtual environment has made it time consuming to acquire relevant information and knowledge amidst a vast amount of information. Therefore, an intelligent system that can offer the most appropriate and desirable among the large amount of information and products by following the conditions and features selected by each user should be essentially efficient. Systems that perform this task are called recommendation systems. Given the volume of social network data, challenges such as short-term processing and increased accuracy of recommendations are discussed in this type of system. Hence, it can perform processes faster with less error and can be effective in improving the performance of social recommending systems in improving the classification and clustering of information with the help of collaboration filtering methods. This study first develops an innovative conceptual model of a social network-based tourism recommendation system using Flicker network data. This model is based on 9 key components. The comparison show that the proposed method has an accuracy of 0.3% and a lower error rate.

Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

Binary matrix completion on graphs: Application to collaborative filtering

This work addresses the problem of completing a partially observed matrix where the entries are either ones or zeroes. This is typically called one-bit matrix completion or binary matrix completion. In this problem, the association among the rows and among the columns can be modeled through graph Laplacians. Since the Laplacians cannot be computed from the incomplete matrix, they must be simultaneously estimated while completing the matrix. We model the problem as graph regularized binary matrix completion where the graphs need to be learnt from the data. We proposed an algorithm based on an alternating minimization scheme, taking advantage of an efficient proximity-based inner solver. The algorithm is applied to the problem of collaborative filtering. Experiments on benchmark datasets with state-of-the-art techniques in collaborative filtering show that the proposed method improves over the rest by a considerable margin.

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

Recommendation 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.

Enhance Web-Based Job Search Recommendation System of Hybrid-Based Recommendation

The main activity carried out daily for an individual to earn money is by working. Finding a job that matches our educational background is not easy. Many individuals do not know their abilities and the limited information on job vacancies is also an obstacle for applicants who want to find work. Therefore, a 'toolkit is needed that can provide recommendations on what fields of work are by the educational background in question. The hybrid approach method is to combine collaborative-filtering techniques (decision tree algorithm) and content-based (nearest neighbor algorithm). The decision tree algorithm is used to classify the fields of work while for job recommendations, the nearest neighbor algorithm is used. In the nearest neighbor, the similarity formula is used to calculate the proximity between applicants and job vacancies based on matching the existing weights and attributes. The output generated from this system is a list of job recommendations that match the applicant's educational background.

Implementing an Enhanced Procurement Management System Using Decision Support Techniques

Modeling procurement management system is important for quality decision making regarding business capacity planning, supply and scheduling. In the Procurement Services Department (PSD), most commonly used indicators to measuring performance include supply period, products rating, ranking recommendations, resource scheduling and number of goods supplied and delivered. In this paper, a Decision Support Service (DSS) technique was proposed to optimize procurement services in business and public organization. First, a study of a typical public and private organization was conducted to gain insights into the operations of the procurement department and the contributions of important system parameters to procurement management. Second, a product and supplier collaborative filtering technique were investigated to obtain transformed data for model training and testing for implementation using Partial Correlation Coefficient (PCC). The Partial Correlation Coefficient (PCC) for a particular product or supplier was utilized for generating the outcomes with tuned values which were compared with actual observed outcomes. The residuals were evaluated in terms of linearity, normality, independence and constant variance. The visualized system plots indicate a good performance as the quality and accuracy of the decision support model was evaluated using some basic metrics. The overall system implementation and performance results demonstrated the importance of Decision Support Services in assessing the performance of procurement management systems. A robust tool for this assessment and a model for procurement and supply planning indicates that the system framework offered Quality of Service (QoS) provisioning.

A frequency count approach to multi-criteria recommender system based on criteria weighting using particle swarm optimization

Collaborative filtering (CF) is the most successful and extensively used recommendation technique in the field of Recommender Systems (RS). Generally, the collaborative filtering technique suffers from sparsity and cold start problems because of the absence of appropriate rating information for efficient similarity computation among users. To handle such issues and to identify similar users more efficiently, it is necessary to include other useful user–item details into the system through efficient fusion methods. The multi-criteria information has been proved as a beneficial and influential factor for improving the performance of the classical CF technique. In this research, we incorporate multi-criteria ratings into traditional CF technique using a frequency count approach. The similarity among users is computed using a newly derived common rating weight similarity (CRS) measure. Further, different users show different importance to various criteria of an item. Therefore, a particle swarm optimization algorithm is used to learn optimal weights on different criteria’s in the process of global similarity computation. The extensive experimental study driven on a benchmark dataset demonstrates significant improvements in prediction and recommendation qualities compared to the most commonly used similarity and heuristic approaches.