Hybrid Recommendation Research Articles

Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation

This paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative filtering. This method not only makes use of the advantages of content filtering but also can carry out similarity matching filtering for all items, especially when the items are not evaluated by any user, which can be filtered out and recommended to users, thus avoiding the problem of early level. At the same time, this method also takes advantage of the advantages of collaborative filtering. When the number of users and evaluation levels are large, the user rating data matrix of collaborative filtering prediction will become relatively dense, which can reduce the sparsity of the matrix and make collaborative filtering more accurate. In this way, the system performance will be greatly improved through the integration of the two. On the basis of the improved collaborative filtering algorithm, a hybrid algorithm based on content and improved collaborative filtering was proposed. By combining user rating with item features, a user feature rating matrix was established to replace the traditional user-item rating matrix. K-means clustering was performed on the user set and recommendations were made. The improved algorithm can solve the problem of data sparsity of traditional collaborative filtering algorithm. At the same time, for new projects, it can also predict users who may be interested in new projects according to the matching of project characteristics and user characteristics scoring matrix and generate push list, which effectively solve the problem of new projects in “cold start.” The experimental results show that the improved algorithm in this paper plays a significant role in solving the speed bottleneck problems of data sparsity, cold start, and online recommendation and can ensure a better recommendation quality.

Hybrid Recommendation System Approach for appropriate developer selection in Bug Repositories

The essential destination of this research is to develop a hybrid recommendation system methodology to enhance the overall performance accuracy of such existed systems, this recommendation approach normally utilized to assign or propose a few counted numbers of programmers or developers that capable of resolving system's bug reports generated automatically from an open source bug repository, meaning the system decides which programmers or developers should be taken into account to be in charge of finding a solution the bugs mentioned in the bug's report. The definition of the bug selection problems in bug repositories are the activities that developers achieve within program maintenance to fix some specific bugs. Because of lot of bugs are created daily, many developers required are quite large, therefore it is difficult to specify the accurate programmers or developers to find a solution for the issues for specific bug inside the code. The article also aims to improve the accuracy results obtained than existed traditional approaches for this purpose. Besides, we have considered the case of prioritization of system developers, the case can be utilized to find an appropriate grade of developers' achievements as prior knowledge to assist the system in assigning of bug report issue. The results have found that the importance of developers could support the bug triage worker more and help software tasks to solve the bugs fast and within required time during development and support cycles of the software.

Modeling and Recommending Open Source Licenses with findOSSLicense

Open source software is widely used in the software industry and the academia. Licenses applied to open source software provide the terms for its further use and distribution. Decisions regarding licensing for new software systems are essential for the system's future use. In this paper, we introduce findOSSLicense, a license recommender that guides users into choosing the appropriate open source license for their software under creation. We also introduce our license modeling concept that is used in the recommendation process. The license modeling captures the properties usually found in existing open source licenses following an analysis performed on license texts. The recommendation process of findOSSLicense is based on a hybrid recommender that uses constraint-based, content-based and collaborative filtering giving also space for flexibility in the use of the system by its end-users who can adapt some system properties. User input, but also external sources of information including existing open source projects, are considered for the creation of the recommendations, whereas licenses used in third party software employed in the software are examined on a limited basis. findOSSLicense has been evaluated with the participation of users of various expertise.

Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System

Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.

A time-aware hybrid recommendation scheme combining content-based and collaborative filtering

A time-aware hybrid recommendation scheme combining content-based and collaborative filtering

A Collaborative Hybrid Recommendation System With Sentimental Analysis For Rumour Detection

In online social networking rumours play a vital role to judge and decide everything. Rumours and Comments are considered to be the opinions of public but most of the rumours are not true and relevant. In the proposed system a hybrid recommendation system with collaboration of sentimental analysis is introduced for eliminating the fake and unwanted rumours. This collaboration model helps to cluster and classify these types of rumour attacks and form a chain link from these predictions to eliminate the rumour. Furthermore the system also projects a verification model to find whether it is a rumour or not.

Design of Hybrid Recommendation Algorithm Based on User Dynamic Behavior and Static Attributes

Collaborative filtering recommendation algorithm is one of the most successful recommendation algorithms, but the traditional collaborative filtering recommendation algorithm is proved to have a series of problems in data sparsity, cold boot and scalability. Based on the problems mentioned above, this paper analyses user dynamic behavior and a hybrid recommendation algorithm based on user dynamic behavior and static attributes (UDBSA) was proposed in this paper. By determining the optimal value of BP critical point for the number of ratings, the recommendation strategy was dynamically selected according to the number of ratings. This method can comprehensively alleviate the influence of problems of new users and concept drift related problem on recommendation results.

Unifying paragraph embeddings and neural collaborative filtering for hybrid recommendation

Collaborative filtering is one of widely used recommendation techniques. Despite the effectiveness of matrix factorization for collaborative filtering; however, the inner product operator, combining the multiplication of latent features linearly, may not be sufficient to capture the complex structure of user interaction ratings. On the other hand, we argue that there is a great deviation between user ratings and their real interest preference. In this paper, we propose a novel hybrid recommendation algorithm. It adopts neural networks to exploit user–item ratings for collaborative filtering, which is endowed a high level of non-linearity for capturing the complex structure of user interaction ratings. At the same time, it exploits item embeddings to capture the content feature for auxiliary information, which solves the cold start problem to some extent. In particular, we introduce paragraph embeddings to represent user reviews and item descriptions, and design two neural networks to capture the sentiment of user reviews and the content feature of items, respectively. And then, we treat these embeddings as attention weights of users and items, and unify them with user–item ratings to model the hybrid recommendation system. Extensive experiments on Amazon product dataset demonstrates that our algorithm performs better on rating prediction than other state-of-the-art algorithms.

A knowledge graph approach for recommending patents to companies

Online platforms have emerged to facilitate patent transfer between academia and industry, but a recommendation method that matches patents with company needs is missing in the literature. Previous patent recommendation methods were designed mainly for query-driven patent search contexts, where user needs are given. However, company needs are implicit in the patent transfer context. The problem of profiling the needs and recommending patents accordingly remains unsolved. This research proposes a knowledge graph approach to address the problem. The proposed approach defines and constructs a patent knowledge graph to capture the semantic information between keywords in the patent domain. Then, it profiles patents and companies as weighted graphs based on the patent knowledge graph. Finally, it generates recommendations by comparing the weighted graphs based on the graph edit distance measure. During the recommendation process, three recommendation strategies (i.e., supplementary, complementary, and hybrid recommendation strategies) are proposed to profile different company needs and make recommendations accordingly. The proposed approach has been implemented and tested on a knowledge transfer platform in Jiangxi province, R.P. China. A pretest experiment shows that the proposed approach outperforms several baseline methods in terms of precision, recall, F-score, and mean average precision. User feedback from an online experiment further demonstrates the usability and the effectiveness of the proposed approach for recommending patents to companies.

A review on Recommender Systems for course selection in higher education

Recommender systems are widely used in many fields. These systems work by recommending a personalized list of items to users based on their interests and thus helping users to overcome excessive information offered to them. For users such as students, selecting the right courses is a very challenging task while joining a new academic level. Picking the wrong courses may affect a student’s academic life as well as their future career. This paper aims at exploring the use of recommender systems to assist students in selecting courses that correspond to their abilities and interests. The results from this review showed that the Hybrid recommendation approach/system could be the best method to help students to choose the right courses in preparation for their future careers.

A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System

Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE+ are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.

A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.

Hybrid semantic recommender system for chemical compounds in large-scale datasets

The large, and increasing, number of chemical compounds poses challenges to the exploration of such datasets. In this work, we propose the usage of recommender systems to identify compounds of interest to scientific researchers. Our approach consists of a hybrid recommender model suitable for implicit feedback datasets and focused on retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares and Bayesian Personalized Ranking) and a new content-based algorithm, using the semantic similarity between the chemical compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of chemical compounds, CheRM-20, with more than 16.000 items (chemical compounds). The hybrid model was able to improve the results of the collaborative-filtering algorithms, by more than ten percentage points in most of the assessed evaluation metrics.

Extending collaborative filtering recommendation using word embedding: A hybrid approach

SummaryCollaborative filtering recommendation systems, which analyze sets of user ratings, have been applied to various domains and have resulted in considerable improvements in the traditional recommendation system. However, they still have problems with data sparsity and cold‐start of the user ratings. To solve these problems, we present a hybrid recommendation approach by combining collaborative filtering methods and word embedding‐based content analysis. This study focuses on the movie domain, and therefore, the contents of the items are represented as a set of features such as titles, genres, directors, actors, and plots. The main aim of this paper is to understand the content of the movie plot using a word embedding to improve the measurement of similarity of each plot content to other plot content (called plot embedding). To enhance the accuracy in measuring the similarity between movies, we also consider other features such as titles, genres, directors, and actors extracted from movies. In the experiments, the movie dataset was collected by our crowdsourcing platform, which is the OMS platform. The experimental findings indicate that the proposed approach can enhance the efficiency of applied collaborative filtering recommendation systems.

A hybrid recommender system based-on link prediction for movie baskets analysis

Over the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

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.

Automatic Recommendation Algorithm for Video Background Music Based on Deep Learning

As one of the traditional entertainment items, video background music has gradually changed from traditional consumption to network consumption, which naturally also has the problem of information overload. From the perspective of model design and auxiliary information, this paper proposes a tightly coupled fusion model based on deep learning and collaborative filtering to alleviate the problem of poor prediction accuracy due to sparse matrix in the scoring prediction problem. In the use of auxiliary information, this paper uses crawler technology to obtain auxiliary information on the user side and the video background music side and compensates for the model’s sensitivity to the sparsity of the score matrix from a data perspective. In terms of model design, this paper conducts auxiliary information mining based on the diversity and structural differences of auxiliary information, uses an improved stack autoencoder to learn user’s interests, and uses convolutional neural networks to mine hidden features of video background music. Based on the idea of probabilistic matrix decomposition, the tightly coupled fusion of multiple deep learning models and collaborative filtering is realized. By comprehensively considering user’s interest and video background music characteristics, the collaborative filtering process is supervised, and the optimized prediction result is finally obtained. The performance test and function test of the system were carried out, respectively, to verify the effectiveness of the hybrid recommendation algorithm and the effect of the system for recommendation. Through experimental analysis, it is proved that the algorithm designed in this paper can improve the recommendation quality and achieve the expected goal.

Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications

Recently, different recommendation techniques in e-learning have been designed that are helpful to both the learners and the educators in a wide variety of e-learning systems. Customized learning, which requires e-learning systems designed based on educational experience that suit the interests, goals, abilities, and willingness of both the learners and the educators, is required in some situations. In this research, we develop an intelligent recommender using split and conquer strategy-based clustering that can adapt automatically to the requirements, interests, and levels of knowledge of the learners. The recommender analyzes and learns the styles and characteristics of learners automatically. The different styles of learning are processed through the split and conquer strategy-based clustering. The proposed cluster-based linear pattern mining algorithm is applied to extract the functional patterns of the learners. Then, the system provides intelligent recommendations by evaluating the ratings of frequent sequences. Experiments were conducted on different groups of learners and datasets, and the proposed model suggested essential learning activities to learners based on their style of learning, interest classification, and talent features. It was experimentally found that the proposed cluster-based recommender improves the recommendation performance by resulting in more lessons completed when compared to learners present in the no-recommender cluster category. It was found that more than 65% of the learners considered all criteria to evaluate the proposed recommender. The simulation of the proposed recommender showed that for learner size values of <1000, better metric values were produced. When the learner size exceeded 1000, significant differences were obtained in the evaluated metrics. The significant differences were analyzed in terms of a computational structure depending on L, the recommendation list size, and the attributes of learners. The learners were also satisfied with the accuracy and speed of the recommender. For the sample dataset considered, a significant difference was observed in the standard deviation σ and mean μ of parameters, in terms of the Recall (List, User) and Ranking Score (User) measures, compared to other methods. The devised method performed well concerning all the considered metrics when compared to other methods. The simulation results signify that this recommender minimized the mean absolute error metric for the different clusters in comparison with some well-known methods.

Hybrid recommender system for tourism based on big data and AI: A conceptual framework

With the development of the Internet, technology, and means of communication, the production of tourist data has multiplied at all levels (hotels, restaurants, transport, heritage, tourist events, activities, etc.), especially with the development of Online Travel Agency (OTA). However, the list of possibilities offered to tourists by these Web search engines (or even specialized tourist sites) can be overwhelming and relevant results are usually drowned in informational "noise", which prevents, or at least slows down the selection process. To assist tourists in trip planning and help them to find the information they are looking for, many recommender systems have been developed. In this article, we present an overview of the various recommendation approaches used in the field of tourism. From this study, an architecture and a conceptual framework for tourism recommender system are proposed, based on a hybrid recommendation approach. The proposed system goes beyond the recommendation of a list of tourist attractions, tailored to tourist preferences. It can be seen as a trip planner that designs a detailed program, including heterogeneous tourism resources, for a specific visit duration. The ultimate goal is to develop a recommender system based on big data technologies, artificial intelligence, and operational research to promote tourism in Morocco, specifically in the Daraa-Tafilalet region.

Research Problems in Recommender systems

With continuous growth of web applications around the globe, it is a challenge to find the suitable information needed for the user in a limited time.Number of handheld mobile devices is increasing and most of the business revolves around the correct search of the data. Without a proper recommender system it is very difficult to get required information from the web applications. Web applications use recommender systems to provide suitable data to users based on their choices and interests. For different kinds of needs different types of recommender systems have been proposed. Two most basic types of recommender systems are collaborative filtering recommender system and content based recommender system. Sometimes these two recommender systems are combined to increase the efficiency of a recommender system The generated new recommender system is known as hybrid recommender system.The purpose of this paper is to help readers understand the basics of recommender systems. This paper identifies key areas of research openly available for new researchers. After reading this paper new researchers can understand basic problems of recommender systems which need improvement and hence they can make those problems their area of research.