Content-based Recommendation Approach Research Articles

Enhancing user experience: a content-based recommendation approach for addressing cold start in music recommendation

Enhancing user experience: a content-based recommendation approach for addressing cold start in music recommendation

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.

Content-Based Movie Recommendation System: An Enhanced Approach to Personalized Movie Recommendations

With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessing of movie metadata from various sources, including genres, actors, directors, and plot summaries. Feature extraction techniques are applied to transform the textual information into meaningful representations that capture the essential characteristics of each movie. Next, a content-based filtering algorithm is employed to compute similarity scores between the user's movie preferences and the extracted features of the available movies. The proposed approach contributes to the advancement of movie recommendation systems and has the potential to enhance user engagement and satisfaction in movie selection.

A content-based recommendation approach based on singular value decomposition

In the Internet era, where information and communication technologies (ICT) allow data exchange, new tools able to select the correct data are needed. In this field, Recommender Systems have a prime location. The recommendation methods take many forms based on the information exploited in order to provide rating forecasts. However, all of them can choose the correct information to support users. Indeed, these methods allow for overcoming the information overload problem. This paper introduces the theoretical bases of a novel recommendation method defined Rating Singular Value Decomposition (RSVD). RSVD is a Content-Based method that exploits the Singular Value Decomposition properties in order to calculate rating forecasts. This method aims to elaborate the users and items profile to obtain matrices related to ones obtained in Collaborative Filtering methods that exploit Singular Value Decomposition. The accuracy of RSVD is compared with the accuracy of Collaborative Filtering methods, and a study on the sparsity problem is performed. The results obtained are promising.

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In this study, a content-based recommendation approach is proposed. It utilizes the preprocessed 245 top movie summaries of IMDB and the favorite movie genres of the user elicited with the questionnaire method and then, recommends potential products -from a product pool- that the user can “like”. For testing; a test dataset that consists of real products from Amazon.com was created, and a Web application that uses the proposed approach and leads the users to evaluate the results of this approach was designed and developed. 52 volunteered subjects attended the test. The subject examined and graded each of the 10 products displayed. Grading was based on the five-level Likert-type scale “Not at all” (0%), “Slightly” (25%), “Moderate” (50%), “Very” (75%), and “Extremely” (100%). It is possible to say that the subjects are moderately liked the products. When the product evaluations are categorized in two categories as “liked” and “disliked”, it is possible to say that the subjects liked ~78.65% of the products. This approach could be integrated into e-commerce applications like Amazon.com for recommending potential products that the user can “like”.

A Hybrid Recommendation Approach for Viral Food Based on Online Reviews.

Nowadays, there are many types of viral foods and consumers expect to be able to quickly find foods that meet their own tastes. Traditional recommendation systems make recommendations based on the popularity of viral foods or user ratings. However, because of the different sentimental levels of users, deviations occur and it is difficult to meet the user’s specific needs. Based on the characteristics of viral food, this paper constructs a hybrid recommendation approach based on viral food reviews and label attribute data. A user-based recommendation approach is combined with a content-based recommendation approach in a weighted combination. Compared with the traditional recommendation approaches, it is found that the hybrid recommendation approach performs more accurately in identifying the sentiments of user evaluations, and takes into account the similarities between users and foods. We can conclude that the proposed hybrid recommendation approach combined with the sentimental value of food reviews provides novel insights into improving the existing recommendation system used by e-commerce platforms.

Social explorative attention based recommendation for content distribution platforms

In modern social media platforms, an effective content recommendation should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. To address the limitations of existing methods for social recommendation, we propose Social Explorative Attention Network (SEAN), a social recommendation framework that uses a personalized content recommendation model to encourage personal interests driven recommendation. SEAN has two versions: (1) SEAN-END2END allows user's attention vector to attend their personalized interested points in the documents. (2) SEAN-KEYWORD extracts keywords from users' historical readings to capture their long-term interests. It is much faster than the first version, more suitable for practical usage, while SEAN-END2END is more effective. Both versions allow the personalization factors to attend to users' higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Constructing two datasets in two languages, English and Spanish, from a popular decentralized content distribution platform, Steemit, we compare SEAN models with state-of-the-art collaborative filtering (CF) and content based recommendation approaches. Experimental results demonstrate the effectiveness of SEAN in terms of both Gini coefficients for recommendation equality and F1 scores for recommendation accuracy.

Hybrid Recommender for Research Papers and Articles

In digital libraries and other e-commerce sites, recommender system is the solution that supports the users in information search and decision making. Some of these recommender systems will make predictions by matching the content of an item against the user profile otherwise known as Content-Based recommendation approach. Other recommenders will provide recommendation based on ratings of items from current user and other users and then use it to recommend similar items the current user has not seen, this is known as Collaborative-Based recommender approach. There exist several other approaches that are used in recommending articles and other items to users of different search engines. Over the years several researchers have tried combining these approaches in an attempt to design more efficient recommendations in search engines. This research proposed and designed a prototype of a Hybrid recommender called Zira, which is a model that combines both the Collaborative filtering, Content-based filtering, attribute-based approach to look at contextual information as well as an item-based approach that will solve the issues associated with cold-start problems all working concurrently to complement one another. The proposed system supports multi-criteria ratings, provide more flexible and less intrusive types of recommendations to ensure the improvement in recommendations of e-learning materials to users of digital libraries.

Content-Based Recommendations for Sustainable Wardrobes Using Linked Open Data

Textile production industry is one of the biggest industries available and it is known by its negative effects to the environment. Greenhouse gas emissions can drastically be reduced by just recycling the textile waste. Such textile recycling has become a lot easier with clothing retailers now starting to offer recycling checkpoints. Moreover, people today are often challenged by overloaded wardrobes and store many clothing items that they never use. In this paper, we describe an Internet of Things system that creates incentives for the users to recycle their clothes, benefiting the environmental sustainability. We propose a content-based recommendation approach that utilizes semantic web technologies and that leverages a set of context signals obtained from the system’s architecture, to recommend clothing items that might be relevant for the user to recycle. Experiments on a real-world dataset show that our proposed approach outperforms a baseline which does not utilize semantic web technologies.

A Model- Based Research Material Recommendation System For Individual Users

As there is an enormous amount of online research material available, finding pertinent information for specific purposes has become a tedious chore. So there is a requirement of the research paper recommendation system to facilitate research scholars in finding their interested and relevant research papers. There are many paper recommendation systems available, most of them are depending on paper assemblage, references, user profile, mind maps. This information is generally not easily available. The majority of the prevailing recommender system is based on collaborative filtering that rely on other user’s proclivity. On the other hand, content-based methods use information regarding an item itself to make a recommendation. In this paper, we present a research paper recommendation method that is based on single paper. Our method uses content-based recommendation approach that employs information extraction and text categorization. . It performs the profile learning by using naive Bayesian text classifier and generates recommendation on the basis of an individual’s preference.

A Personalization-Oriented Academic Literature Recommendation Method

As the number of digital academic items increases dramatically, it is more and more difficult for a student or researcher to find the expected references in a large academic literature database. Although collaborative filtering and content-based recommendation approaches perform well in some applications, they do not produce satisfactory recommendations for academic items because they fail to reflect researchers’ unique characteristics in terms of authority, popularity, recentness, etc. In this paper, we propose two novel data structures, ALVector, which expresses various objective attributes of an article, and AUVector, which expresses users’ subjective weights for different attributes. Then, we propose a novel personalization-oriented recommendation method that utilizes both the content and non-content attributes in ALVector and AUVector for making recommendations. In order to make the overall best recommendation, the VIKOR algorithm is used with a personalization-oriented method to achieve a compromise solution. A real-world literature data set is used in the experiments. The experimental results show that our method better meets the user’s preference in multiple dimensions simultaneously.

Semantic content-based recommendation of software services using context

The current proliferation of software services means users should be supported when selecting one service out of the many which meet their needs. Recommender Systems provide such support for selecting products and conventional services, yet their direct application to software services is not straightforward, because of the current scarcity of available user feedback, and the need to fine-tune software services to the context of intended use. In this article, we address these issues by proposing a semantic content-based recommendation approach that analyzes the context of intended service use to provide effective recommendations in conditions of scarce user feedback. The article ends with two experiments based on a realistic set of semantic services. The first experiment demonstrates how the proposed semantic content-based approach can produce effective recommendations using semantic reasoning over service specifications by comparing it with three other approaches. The second experiment demonstrates the effectiveness of the proposed context analysis mechanism by comparing the performance of both context-aware and plain versions of our semantic content-based approach, benchmarked against user-performed selection informed by context.

MINGLING THE CONTEXTUAL INFORMATION IN IMPROVED MULTIDIMENSIONAL RECOMMENDATION SYSTEM

Recommender systems utilize the times of yore experiences and preferences of the target customers as a basis to proffer personalized recommendations for them as well as resolve the information overloading hitch. Personalized recommendation methods are primarily classified into content-based recommendation approach and collaborative filtering recommendation approach. Both recommendation approaches have their own advantages, drawbacks and complementarities. Because conventional recommendation techniques don’t consider the contextual information, the real factor why a customer likes a specific product is unable to be understood. Therefore, in reality, it often causes a decrease in the accuracy of the recommendation results and also persuades the recommendation quality. In this paper, we propose the integrated contextual information as the foundation concept of multidimensional recommendation model and use the Online Analytical Processing (OLAP) ability of data warehousing to solve the contradicting tribulations among hierarchy ratings. This work hopes that by establishing additional user profiles and multidimensional analysis to find the key factors affecting user perceptions, it would increase the recommendation quality.

A Personalized Recommender Systems Framework Based on Ontology and Bayesian Network in E-Commerce

Personalized recommendation methods are mainly classified into content-based recommendation approach and collaborative filtering recommendation approach. However, Both recommendation approaches have their own drawbacks such as sparsity, cold-start and scalability. To overcome the drawbacks, In this paper, we propose a framework for recommender systems that join use of Ontology and Bayesian Network. On the one hand, Ontology help formally defining the semantics of variables included in the Bayesian network, thus allowing logical reasoning on them. On the other hand, Bayesian network allow reasoning under uncertainty, that is not possible only with the use of ontology. In the recommendation, products not yet purchased or rarely purchased can still be recommended to customers with accuracy.

Using contextual information and multidimensional approach for recommendation

It has been recognized that recommendation system is a very important and indispensable topic in E-commerce. Many famous E-commerce websites utilize recommendation systems to convert browsers into buyers. The forms of recommendation include suggesting products/services to the customer, providing personalized product/service information, summarizing community opinion, and providing community critiques. Personalized recommendation methods are mainly classified into content-based recommendation approach and collaborative filtering recommendation approach. Both recommendation approaches, however, have their own drawbacks. This study proposes the integrated contextual information as the foundation concept of multidimensional recommendation model, and uses the online analytical processing (OLAP) ability of data warehousing to solve the contradicting problems among hierarchy ratings. The evaluation studies show that by establishing additional customer profiles and using multidimensional analyses to find the key factors affecting customer perceptions, the proposed approach increases the recommendation quality.