Hybrid Recommendation Algorithm Research Articles

A hybrid recommendation algorithm based on user nearest neighbor model

In the realm of e-commerce, personalized recommendations are a crucial component in enhancing user experience and optimizing sales efficiency. To address the inherent sparsity challenge prevalent in collaborative filtering algorithms within personalized recommendation systems, we propose a novel hybrid e-commerce recommendation algorithm based on the User-Nearest-Neighbor model. By integrating the user nearest neighbor model with other recommendation algorithms, this approach effectively mitigates data sparsity and facilitates a more nuanced understanding of the user-product relationship, consequently elevating recommendation quality and enhancing user experience. Taking into account considerations such as data scale and recommendation performance, we conducted experiments utilizing the Spark distributed platform. Empirical findings demonstrate the superiority of our hybrid algorithm over standalone collaborative filtering algorithms across various recommendation indicators.

Research on Hybrid Recommendation Algorithm based on Collaborative Filtering and Spearman Rank Correlation Coefficient

A recommendation system is an information filtering tool that helps users find the products or services they need from a large amount of information. However, collaborative filtering is quite sensitive to data sparsity and cold start problems, and it may encounter certain difficulties when handling outliers. To address the issue of handling outliers, it is necessary to study and improve the existing collaborative filtering techniques. This paper proposes a personalized recommendation algorithm that integrates collaborative filtering with the Spearman rank correlation coefficient. By combining collaborative filtering and the Spearman rank correlation coefficient, the algorithm uses the latter to handle outliers, making it more suitable for nonlinear relationships. This hybrid recommendation algorithm can better handle outliers while maintaining personalized recommendations, providing a basis and reference for personalized recommendations.

Dynamic Personalized Recipe Recommendations Based on Facial Health Recognition

Nowadays, the demand for more personalized healthy food recipes is increasing over time, but traditional personalized recipe recommendation systems often tend to overlook the differences that exist in different users’ health conditions. This paper aims to address the issue by proposing an innovative personalized health recipe recommendation framework. This framework is based on different health needs, aiming to address users with different health conditions. More explicitly, the hybrid recommendation algorithm based on facial health recognition is designed to recommend the most suitable recipes for the user by taking into account the user’s health status and preferences. In addition, the fitness factor will adjust the recommendation results to best meet the user’s taste preferences and health goals. Experimental results and user research results show that the recommendation results of this framework are more accurate compared to existing systems, and therefore, users are more satisfied.

Improved GMMKNN Hybrid Recommendation Algorithm

This paper proposes a hybrid recommendation algorithm that combines the advantages of Gaussian Mixture Model (GMM) and K-Nearest Neighbors (KNN) algorithms. The algorithm first applies GMM to cluster the training data, grouping users with similar interests using clustering techniques. It then utilizes the KNN algorithm for prediction. During the KNN recommendation process for a target user, the algorithm searches for neighboring users with similar interests and features within the same cluster. This significantly reduces the search scope of the nearest neighbors, thanks to the assistance of the GMM algorithm. The algorithm then selects the K most similar neighbors from the target user’s cluster as the candidate pool for personalized recommendations. Additionally, the algorithm improves the weight calculation method by incorporating a Gaussian kernel function for weight estimation. Experimental results on the dataset demonstrate that the proposed improved algorithm effectively enhances the accuracy of prediction results.

Hybrid Recommendation Algorithm based on Product Popularity and User Preference

In today's society, recommendation system is widely used to recommend users' preferred products and content. However, with the rapid development of the Internet, there are more and more contents on the Internet, and the algorithm complexity of recommending products to users through the recommendation system is getting higher and higher, and the recommendation algorithm is also more and more inclined to recommend popular products to users, which also leads to the decline in the novelty and diversity of the recommendation algorithm. In order to solve this problem, the study first adopts the method of inhibiting the recommendation ability of current popular products, and on this basis, introduces the influence of user rating on the recommendation ability of different products. At the same time, the product is classified, take the movie for example, can be classified as action film, comedy, children's film and so on. There is a very high probability that an adult will not like children's films, and a very high probability that a child will not like horror films. Impose a penalty on a product category that the user may not like, reducing the likelihood that the product will be recommended to the user. The proposed algorithm is compared with other excellent algorithms in this field on MovieLens movie score dataset, and the results of the experiments showed that the algorithm improved in accuracy, diversity, and novelty.

Research and application analysis of typical recommendation methods

Recommendation systems are vital tools in the modern digital landscape for handling vast amounts of online data and are now employed across a variety of sectors. This manuscript underlines the importance of video recommendation algorithms and delves into the three main types: content-based, collaborative filtering, and hybrid algorithms. Content-based algorithms offer suggestions by assessing and matching the descriptive attributes of videos. These attributes could range from the genre, the actors involved, the director, or any other related metadata. On the other hand, collaborative filtering algorithms provide recommendations by comparing the user's historical data with that of others. They function on the principle that individuals who have agreed in the past are likely to agree again in the future. Hybrid recommendation algorithms, as the name suggests, are a combination of both content-based and collaborative filtering algorithms. They aim to harness the strengths of the two while mitigating their individual weaknesses, providing more balanced and accurate recommendations. This detailed exploration of these algorithms not only underscores their significance but also paves the way for the future, heralding the potential advancements and adaptations these systems could undergo. Continued innovation in this realm has the potential to revolutionize how information is processed and shared, ultimately enriching the user experience across the digital platform landscape.

Hybrid Recommender Systems based on Content Feature Relationship

Recommendation systems get ever-increasing importance due to their applications in both academia and industry. The most popular type of these systems, known as collaborative filtering algorithms, employ user-item interactions to perform the recommendation tasks. With growth of additional information sources other than the rating (or purchase) history of users on items, such as item descriptions and social media information, further extensions of these systems have been proposed, known as hybrid recommendation algorithms. Hybrid recommenders use both user-item interaction data and their contextual information. In this work, we propose new hybrid recommender algorithms by considering the relationship between content features. This relationship is embedded into the hybrid recommenders to improve their accuracy. We first introduce a novel method to extract the content feature relationship matrix, and then the collaborative filtering recommender is modified such that this relationship matrix can be effectively integrated within the algorithm. The proposed algorithm can better deal with the cold-start problem than the state-of-art algorithms. We also propose a novel content-based hybrid recommender system. Our experiments on a benchmark movie dataset show that the proposed approach significantly improves the accuracy of the system, while resulting in satisfactory performance in terms of novelty and diversity of the recommendation lists.

A study on a recommendation algorithm based on spectral clustering and GRU

With the development of e-commerce, the importance of recommendation algorithms has significantly increased. However, traditional recommendation systems struggle to address issues such as data sparsity and cold start. This article proposes an optimization method for a recommendation system based on spectral clustering (SC) and gated recurrent unit (GRU), named the GRU-KSC algorithm. Firstly, this paper improves the original spectral clustering algorithm by introducing Kmc2, proposing a novel spectral clustering recommendation algorithm (K-means++ SC, KSC) based on the existing SC algorithm. Secondly, building upon the original GRU model, the paper presents a hybrid recommendation algorithm (Hybrid GRU, HGRU) capable of capturing long-term user interests for a more personalized recommendation. Experiments conducted on real datasets demonstrate that our method outperforms existing benchmark methods in terms of accuracy and robustness.

Experimental Hybrid Approach for Improving the Quality of Personalized Product Recommendation Systems with Deep Learning

Deep learning has recently made significant advancements in recommender systems. Various methods, including hybrid recommendation systems, content-based recommendations, and deep learning, have been employed to enhance recommendation accuracy. Over the past decade, the proliferation of big data has led to a rapid increase in the amount of available data on the internet. However, dealing with complex and large data sets makes it challenging for many users to quickly find the information they need. At this juncture, recommendation systems play a crucial role in addressing the issue of data overload. The development of recommendation algorithms has been particularly driven by the growth of the e-commerce industry. Traditional single recommendation algorithms face challenges such as data sparsity, long-tail items, and cold starts. Hybrid recommendation algorithms can effectively mitigate some of these limitations. In response to these challenges, this paper proposes an experimental hybrid approach to improve the quality of personalized product recommendations using deep learning, specifically an Improved Attention-Convolutional Neural Network (IA-CNN). This method aims to address the shortcomings of single collaborative models. The algorithm first employs a comprehensive approach, combining product- and user-based collaborative filtering strategies to generalize and categorize the output results. This methodology captures detailed and abstract nonlinear interactions between users and products through advanced deep learning techniques. Finally, experiments were designed to test the algorithm's effectiveness. The IA-CNN algorithm was evaluated against a benchmark algorithm using the Amazon product rating dataset. The results demonstrate that the proposed IA-CNN algorithm outperforms the benchmark in rating prediction on the test dataset

A Study of Digital Museum Collection Recommendation Algorithm Based on Improved Fuzzy Clustering Algorithm

With the rapid advancement of internet technology, various industries have accumulated vast amounts of data, including on user behavior and personal preferences. Traditional museums can leverage this user data to uncover individual preferences and offer personalized services to their visitors. However, the exponential growth of information has also led to the problem of information overload, making it challenging for users to find relevant information within the vast data landscape. Consequently, the utilization rate of available information decreases. By harnessing the power of cloud computing, big data analytics, and recommendation systems, museums can enhance visitors’ touring experiences by helping them discover collections aligned with their interests and connecting with like-minded individuals. To address this objective, the research focuses on optimizing the initial clustering centers of the fuzzy clustering algorithm and parallelizing the optimized algorithm using MapReduce, resulting in the development of a novel MapReduce-based k-prototype fuzzy c-means (MRKPFCM) algorithm. Subsequently, the MRKPFCM algorithm is combined with the classical collaborative filtering algorithm to create a hybrid and parallelized collaborative filtering recommendation algorithm, incorporating elements such as MRKPFCM, audience, and collection. This hybrid algorithm is further supplemented by a content-based recommendation approach to generate comprehensive and refined recommendation results. Experimental findings demonstrate that the predictive scoring errors, as measured by RMSE and MAE, exhibited a downward trend when the number of nearest neighbors for target users fell within the range of 10–20. For instance, the studied algorithm’s MAE value decreased from 0.7512 to 0.7179, surpassing the corresponding figures for the two comparison algorithms. Moreover, with an increase in the number of nearest neighbors within the same range, all three algorithms experienced improved accuracy in prediction results. In particular, the accuracy rate rose from 17.84% to 18.82%, outperforming the two comparison algorithms. In summary, the enhanced hybrid recommendation algorithm achieved through this study displays superior recommendation accuracy and holds significant practical value.

Toward topic diversity in recommender systems: integrating topic modeling with a hashing algorithm

PurposeImproving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.Design/methodology/approachThis paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.Findings(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.Originality/valueThe hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

A hybrid recommendation algorithm for green food based on review text and review time

ABSTRACT Green food is well-known for its health benefits, environmental-friendliness, and safety. Current recommender systems used by e-commerce websites usually recommend products based on products' popularity or customers' ratings. However, users' reviews could be more representative of consumers' preferences. In addition, users' review time is not utilized. To reduce the recommendation bias, this study proposes a hybrid recommendation algorithm based on green food reviews and review time. The proposed algorithm combines a content-based recommendation algorithm with a user-based collaborative filtering approach, where affective values of reviews replace ratings and a time impact factor is considered. With the two classical evaluation indices of F1 and Mean Absolute Error (MAE), the experiments show that considering both reviews’ sentiments and dynamic changes of individuals’ preferences could improve recommendation effectiveness over three other algorithms, which provides a new reference direction for improving existing recommender systems on green food.

A hybrid recommendation algorithm based on user comment sentiment and matrix decomposition

In order to improve recommendation quality of recommendation algorithms, this paper proposes a hybrid recommendation algorithm based on user comments sentiment and matrix decomposition (abbreviate as RACSMD). This algorithm first calculates the sentiment tendency towards the user’s comment through the LSTM algorithm, and then integrates the sentiment value of the user’s rating to increase the accuracy of the user’s actual rating before combining the matrix decomposition recommendation algorithm to improve recommendation quality. This paper theoretically verifies the feasibility of RACSMD through an algorithm example. Moreover, corresponding experimental analysis is conducted on the basis of three data sets of Beeradvocate, Modcloth and Amazon. Experimental results show that the introduction to sentiment tendencies towards user comments can effectively improve recommendation quality of recommendation algorithms.

Algorithm Improvement of Movie Recommendation System based on Hybrid Recommendation Algorithm

In recent years, the Internet has developed rapidly, and in the face of thousands of data and information, it has become very critical for users to find the information that is of high value to them in the mass of information, and the recommendation system is one of the most effective ways to solve this information overload phenomenon. In this paper, the current movie recommendation algorithm is improved by using an item-based collaborative filtering algorithm for the similarity measure of items in the item-based recommendation process; In the recommendation process, two more applicable recommendation methods are considered: collaborative filtering content-based recommendation and matrix decomposition-based recommendation. It saves users time in searching, viewing and filtering, while discovering information about their potential movie preferences.

CCRMDA: MiRNA-disease Association Prediction Based on Cascade Combination Recommendation Method on a Heterogeneous Network

Background: MicroRNAs (miRNAs) are a class of short and endogenous single-stranded non-coding RNAs, with a length of 21-25nt. Many studies have proved that miRNAs are closely related to human diseases. Many algorithms based on network structure have been proposed to predict potential miRNA-disease associations. Methods: In this work, a cascade combination method based on network topology is developed to explore disease-related miRNAs. We name our method as CCRMDA. First, the hybrid recommendation algorithm is used for a rough recommendation, and then the structural perturbation method is used for a precise recommendation. A special perturbation set is constructed to predict new miRNA-disease associations in the miRNA-disease heterogeneous network. Results: To verify the effectiveness of CCRMDA, experimental analysis is performed on HMDD V2.0 and V3.2 datasets, respectively. For HMDD V2.0 dataset, CCRMDA is compared with several state-ofthe- art algorithms based on network structure, and the results show that CCRMDA has the best performance. The CCRMDA method also achieves excellent performance with an average AUC of 0.953 on HMDD V3.2 dataset. In addition, case studies further prove the effectiveness of CCRMDA. Conclusion: CCRMDA is a reliable method for predicting miRNA-disease.

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

In the MOOCs context, learners experience information overload. Thus, it is necessary to improve personalized recommendation algorithms for learners. The current recommendation algorithm focuses mainly on the learners’ course ratings. However, the choice of courses is not only based on the learners’ interests and preferences. It is also affected by learners’ knowledge domains and learning capabilities, all of which change dynamically over time. Therefore, this study proposes a personalized hybrid recommendation algorithm combining clustering with collaborative filtering. First, data on learners’ course rating preferences, course attribute preferences, and multidimensional capabilities that match course traits are used based on multidimensional item response theory. Second, considering that learners’ preferences and multidimensional capabilities change dynamically over time, the Ebbinghaus forgetting curve is introduced by integrating memory weights to improve the accuracy and interpretation of the proposed recommendation algorithm for MOOCs. Finally, the performance of the proposed recommendation algorithm is investigated using data from Coursera, an internationally renowned MOOCs platform. The experimental results show that the proposed recommendation algorithm is superior to the baseline algorithms. Accordingly, relevant suggestions are proposed for the development of MOOCs.

Research on personalized recommendation algorithm for micromechanical sensors based on cloud model

Abstract In order to improve the ability of micromechanical sensors to update data-matching nodes in real-time and speed up the establishment of a neighbor relationship between nodes, this paper proposes a personalized recommendation algorithm for micromechanical sensors based on the cloud model to improve the stability and real-time performance of micromechanical sensors. The algorithm uses the service attribute values of the cloud computing model and cloud clustering method to set feature term labels and establish a cloud service similarity matrix so as to meet the user-matched manufacturing service requirements. The intra-class clustering technique is applied to measure the clustering effect, and the evaluation function of each clustering number is calculated on the basis of considering the time sequence to determine the best clustering result to complete the personalized recommendation path for micromechanical sensors. To verify the application effect of the personalized recommendation algorithm for micromechanical sensors based on the cloud model, experiments are conducted. The results show that the recommendation algorithm in this paper can always control the node energy consumption below 2.5×103, and the average discovery delay is stable between 41-43 seconds. And the sensor response time is 12.1 seconds, and the average absolute deviation value is 0.23, which is nearly 1.3 times smaller than 0.53 and 0.52 of the collaborative recommendation algorithm and hybrid recommendation algorithm. It can be seen that the recommendation algorithm in this paper solves the problem of excessive neighbor discovery delay in the communication process of micromechanical sensors and effectively improves the personalized recommendation performance of micromechanical sensors.

A Novel Hybrid Recommender System for the Tourism Domain

In this paper, we develop a novel hybrid recommender system for the tourism domain, which combines (a) a Bayesian preferences elicitation component which operates by asking the user to rate generic images (corresponding to generic types of POIs) in order to build a user model and (b) a novel content-based (CB) recommendations component. The second component can in fact itself be considered a hybrid among two different CB algorithms, each exploiting one of two semantic similarity measures: a hierarchy-based and a non-hierarchy based one. The latter is the recently introduced Weighted Extended Jaccard Similarity (WEJS). We note that WEJS is employed for the first time within a recommender algorithm. We incorporate our algorithm within a real, already available at Google Play, tour-planning mobile application for short-term visitors of the popular touristic destination of Agios Nikolaos, Crete, Greece, and evaluate our approach via extensive simulations conducted on a real-world dataset constructed for the needs of the aforementioned mobile application. Our experiments verify that our algorithms result in effective personalized recommendations of touristic points of interest, while our final hybrid algorithm outperforms our exclusively content-based recommender algorithms in terms of recommendations accuracy. Specifically, when comparing the performance of several hybrid recommender system variants, we are able to come up with a “winner”: the most preferable variant of our hybrid recommender algorithm is one using a ⟨four elicitation slates, six shown images per slate⟩ pair as input to its Bayesian elicitation component. This variant combines increased precision performance with a lightweight preferences elicitation process.

LFDNN: A Novel Hybrid Recommendation Model Based on DeepFM and LightGBM

Hybrid recommendation algorithms perform well in improving the accuracy of recommendation systems. However, in specific applications, they still cannot reach the requirements of the recommendation target due to the gap between the design of the algorithms and data characteristics. In this paper, in order to learn higher-order feature interactions more efficiently and to distinguish the importance of different feature interactions better on the prediction results of recommendation algorithms, we propose a light and FM deep neural network (LFDNN), a hybrid recommendation model including four modules. The LightGBM module applies gradient boosting decision trees for feature processing, which improves LFDNN's ability to handle dense numerical features; the shallow model introduces the FM model for explicitly modeling the finite-order feature crosses, which strengthens the expressive ability of the model; the deep neural network module uses a fully connected feedforward neural network to allow the model to obtain more high-order feature crosses information and mine more data patterns in the features; finally, the Fusion module allows the shallow model and the deep model to obtain a better fusion effect. The results of comparison, parameter influence and ablation experiments on two real advertisement datasets shows that the LFDNN reaches better performance than the representative recommendation models.

A Thematic Travel Recommendation System Using an Augmented Big Data Analytical Model

Tour planning has become both challenging and time-consuming due to the huge amount of information available online and the variety of options to choose from. This is more so as each traveler has unique set of interests and location preferences in addition to other tour-based constraints such as vaccination status and pandemic travel restrictions. Several travel planning companies and agencies have emerged with more sophisticated online services to capitalize on global tourism effectively by using technology for making suitable recommendations to travel seekers. However, such systems predominantly adopt a destination-based recommendation approach and often come as bundled packages with limited customization options for incorporating each traveler’s preferences. To address these limitations, “thematic travel planning” has emerged as a recent alternative with researchers adopting text-based data mining for achieving value-added online tourism services. Understanding the need for a more holistic theme approach in this domain, our aim is to propose an augmented model to integrate analytics of a variety of big data (both static and dynamic). Our unique inclusive model covers text mining and data mining of destination images, reviews on tourist activities, weather forecasts, and recent events via social media for generating more user-centric and location-based thematic recommendations efficiently. In this paper, we describe an implementation of our proposed inclusive hybrid recommendation model that uses data of multimodal ranking of user preferences. Furthermore, in this study, we present an experimental evaluation of our model’s effectiveness. We present the details of our improvised model that employs various statistical and machine learning techniques on existing data available online, such as travel forums and social media reviews in order to arrive at the most relevant and suitable travel recommendations. Our hybrid recommender built using various Spark models such as naïve Bayes classifier, trigonometric functions, deep learning convolutional neural network (CNN), time series, and NLP with sentiment scores using AFINN (sentiment analysis developed by Finn Årup Nielsen) shows promising results in the directions of benefit for an individual model’s complementary advantages. Overall, our proposed hybrid recommendation algorithm serves as an active learner of user preferences and ranking by collecting explicit information via the system and uses such rich information to make personalized augmented recommendations according to the unique preferences of travelers.