User-based Collaborative Filtering Method Research Articles

SISTEM REKOMENDASI PEMINJAMAN BUKU DENGAN METODE USER-BASED COLLABORATIVE FILTERING PADA PERPUSTAKAAN SEKOLAH BERBASIS WEBSITE

Book Lending Recommendation System Using User-Based Collaborative Filtering Method in Website-Based School Libraries.The purpose of this research is to design and implement a Book Lending Recommendation System with User-Based Collaborative Filtering Method in School Libraries. This data was obtained through: 1) Observation, 2) Interview, 3) Documentation. By using the User-Based Collaborative Filtering method in making this application. In making this application the User-Based Collaborative Filtering method is used to make recommendations based on book ratings given by library members.The results of this study indicate that the application of the Book Lending Recommendation System at the website-based school library with the User-Based Collaborative Filtering method is declared feasible to use with the percentage results on the testing aspects with a percentage rate of 84% getting a good response.

SISTEM REKOMENDASI DRAMA KOREA MENGGUNAKAN METODE USER-BASED COLLABORATIVE FILTERING

Korean drama is one of the entertainment that is very popular with the public, be it among children, teenager, adults and parents. There is a huge selection of dramas to watch, however, viewers have limited time. Therefore, a recommendation system is used to provide input to viewers in choosing Korean drama series that suits their respective profiles. This recommendation system is made using the User-Based Collaborative Filtering method, where the input of this method is in the form of rating data provided by the user for a list of available Korean dramas. Based on the results of interviews via video calls and questionnaires, this Korean drama application can provide different recommendation results based on user ratings of Korean dramas.

Integrated AHP-IOWA, POWA Framework for Ideal Cloud Provider Selection and Optimum Resource Management

The lack of a common framework often complicates the process of provider selection and marginal resource allocation decision. The nonlinear relationships among selection criteria greatly impact the decision-making process. The paper address the critical issue by proposing a centralised Quality of Experience (QoE) and Quality of Service (QoS)- CQoES framework. The framework considers customised priority criteria, determine the relative importance of each criterion and intelligently assign relative weights to each criterion. The framework assists the service provider to in decision making for marginal resources. To achieve the objective, we employ the Analytical Hierarchical Process (AHP), Induced OWA (IOWA) operator, Probabilistic OWA (POWA) operator, user-based collaborative filtering method with enhanced top KNN algorithm. The method handles complex nonlinear relationship of the selection criteria. It signifies consumer's customised criteria in relation to other criteria, then reorders inputs based on the ordered-inducing variable. The proposed method smartly unifies the provider's probabilistic information and the attitudinal characteristics for marginal resource allocation. To demonstrate the effectiveness of the approach, we present two scenarios and use a real cloud and other web service dataset. The experimental results show that the proposed system handles the issue of service selection and marginal resource allocation decision.

Design and test music recommendation system for online music websites using collaborative filtering approach

The music industry is currently growing very rapidly. The limited time possessed by consumers to consume products conventionally is the main reason for online purchases by consumers. This change also occurred in the music industry, so that online music sites that provide convenience in service and this convenience made consumers decide to buy music products online and abandoned the old habit of consuming conventional music products on CDs. The ease of consuming songs online does not make the user easy to determine the songs they want to hear. The difficulty of users in determining what songs to listen to in a web music can be overcome by the existence of a music recommendation system, where music sites will provide recommendations on songs based on user preferences. This study aims to create a music recommendation system on online music sites to provide convenience for consumers in choosing music products according to their preferences. This study explains the problem of users of online music sites who do not rank a song that has been heard, and overcome it with a recommendation system using user-based collaborative filtering methods.

Enhanced Digital Library with Book Recommendations Based on Collaborative Filtering

This research aims to develop a digital library system by implementing a recommendation system becomes a feature of the digital library system. The main problem with the digital library system is the large number of books that users will have difficulties finding interesting books. Therefore, Machine Learning is applied using the User-based Collaborative Filtering method to provide book recommendations for users. The book recommendation system being developed not only uses book value as input but also user behavior and book borrowing data. The resulting recommendations are divided into three parts according to the input which are book value, user behavior, and book borrowing data. The system developed is web-based using the PHP programming language, with modifications to the available open-source digital library system. Experiments are carried out by testing the system to university students and satisfaction questionnaire to the students. The total result of the average precision-recall calculation has an accuracy of 79% from 35 data users which includes an assessment of 1000 types of books with different categories. It means that books recommended by the system are relevant to users. The developed models and applications are potentially used as smart library applications.

Designing of Recommendation Engine for Recyclable Waste Mobile App

The objective of this research is to design a recommendation engine for Pilah Matur App. The recommendation engine in this research used a combination of CF (Collaborative Filtering) and LBS (Location Based Service). Collaborative filtering performs data filtering based on the similarity of user characteristics so that it is able to provide new information to other users because CF provides information based on a pattern of one user group that has a similarity. The preference for recycled material used the nearest neighbor similarity method based on GPS coordinates where the recycled material was uploaded by the recyclable waste donor or volunteer then compared to the location of the recyclable waste taker. The recommendations proposed by collaborative filtering methods could be measured for accuracy using Mean Absolute Error (MAE). The results of the MAE calculation in the user-based CF method, the App Maturity dataset has an MAE value of 0.33. While the item-based CF method gets an MAE value of 0.17 using the same dataset. The results of testing the CF method show that in user-based collaborative filtering the prediction errors are more than in item-based collaborative filtering. The results of the CF method recommendations then sorted based on the closest distance by the LBS method.

Modeling Long-Term User Profile in Collaborative Filtering

Collaborative filtering methods are widely accepted and used for item recommendation in various applications and domains. Their simplicity and ability to provide recommendations without the need for specific domain knowledge makes them utilized in both academic and industrial community. However, continuous dynamics of the system makes them vulnerable on different kind of changes, such as the change in user’s preferences or the appearance of new users and items in the system. It is particularly emphasized in user-based collaborative filtering recommendations which are based on both users and its nearest neighbor’s long-term profiles. In this work an approach is presented in order to recognize some of the changes and provide an upgraded model that provides improved performance. Such changes include deviations in user’s mean ratings, changes in neighbors’ similarities, as well as deviations from neighbors’ mean rating. For each of the proposed improvements a set of matching parameters are identified that form a long-term user’s profile. The performance of the proposed models compared to standard user-based collaborative filtering methods is evaluated in terms of prediction accuracy. The experimental results performed on real data set show sound improvement utilizing adjustment of deviations from user’s mean ratings and the neighbor’s similarities, as well as promising improvement with adjustment of neighbor’s mean ratings.

A Hybrid Social Network-based Collaborative Filtering Method for Personalized Manufacturing Service Recommendation

Nowadays, social network-based collaborative filtering (CF) methods are widely applied to recommend suitable products to consumers by combining trust relationships and similarities in the preference ratings among past users. However, these types of methods are rarely used for recommending manufacturing services. Hence, this study has developed a hybrid social network-based CF method for recommending personalized manufacturing services. The trustworthy enterprises and three types of similar enterprises with different features were considered as the four influential components for calculating predicted ratings of candidate services. The stochastic approach for link structure analysis (SALSA) was adopted to select top K trustworthy enterprises while also considering their reputation propagation on enterprise social network. The predicted ratings of candidate services were computed by using an extended user-based CF method where the particle swarm optimization (PSO) algorithm was leveraged to optimize the weights of the four components, thus making service recommendation more objective. Finally, an evaluation experiment illustrated that the proposed method is more accurate than the traditional user-based CF method.

Item-network-based collaborative filtering: A personalized recommendation method based on a user's item network

Recommendation systems are becoming important with the increased availability of online services. A typical approach used in recommendations is collaborative filtering. However, because it largely relies on external relations, such as items-to-items or users-to-users, problems occur when the relations are biased or insufficient. Focusing on that limitation, we here suggest a new method, item-network-based collaborative filtering, which recommends items through four steps. First, the system constructs item networks based on users’ item usage history and calculates three types of centrality: betweenness, closeness, and degree. Next, the system secures significant items based on the betweenness centrality of the items in each user's item network. Then, by using the closeness and degree centrality of the secured items, the algorithm predicts preference scores for items and their rank orders from each user's perspective. In the last step, the system organizes a recommendation list based on the predicted scores. To evaluate the performance of our system, we applied it to a sample dataset of 196 Last.fm users’ listening history and compared the results with those from existing collaborative filtering methods. The results showed that the suggested method performed better than the basic item-based and user-based collaborative filtering methods in terms of Accuracy, Recall, and F1 scores for top-k recommendations. This indicates that an individual user's item relations can be utilized to remedy the problems occurring when the external relations are biased or insufficient.

A Rating-Based Integrated Recommendation Framework with Improved Collaborative Filtering Approaches

Collaborative filtering (CF) approach is successfully applied in the rating prediction of personal recommendation. But individual information source is leveraged in many of them, i.e., the information derived from single perspective is used in the user-item matrix for recommendation, such as user-based CF method mainly utilizing the information of user view, item-based CF method mainly exploiting the information of item view. In this paper, in order to take full advantage of multiple information sources embedded in user-item rating matrix, we proposed a rating-based integrated recommendation framework of CF approaches to improve the rating prediction accuracy. Firstly, as for the sparsity of the conventional item-based CF method, we improved it by fusing the inner similarity and outer similarity based on the local sparsity factor. Meanwhile, we also proposed the improved user-based CF method in line with the user-item-interest model (UIIM) by preliminary rating. Second, we put forward a background method called user-item-based improved CF (UIBCF-I), which utilizes the information source of both similar items and similar users, to smooth itembased and user-based CF methods. Lastly, we leveraged the three information sources and fused their corresponding ratings into an Integrated CF model (INTE-CF). Experiments demonstrate that the proposed rating-based INTE-CF indeed improves the prediction accuracy and has strong robustness and low sensitivity to sparsity of dataset by comparisons to other mainstream CF approaches.

Collaborative filtering recommendation based on conditional probability and weight adjusting

Collaborative filtering recommendation algorithm is one of the most successful technologies for building recommender systems. However, a user-based collaborative filtering method has its limits related to similarity and ratings. To avoid those limits, we propose a new item-based collaborative filtering algorithm based on conditional probability and weight adjusting in this paper. At first, any two items are selected to compute the similarity from common user ratings, and only the items with the similarity greater than preset thresholds are chosen as the set of supporting items. Then an integral parameter, that is the frequency of two items present simultaneously, is used to adjusted similarity weights. Finally, a new algorithm combining conditional probability and weight adjusting is proposed to predict ratings. The experimental results show the proposed algorithm is feasible and effective in practice.

Information filtering via biased random walk on coupled social network.

The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users' preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods.

A novel user-based collaborative filtering method by inferring tag ratings

User-based collaborative filtering is one of the most widely-used recommendation methods. It recommends items to a user based on her similar users' preferences. The essential part of user-based collaborative filtering is to infer users' similarities. A common method is to compute the similarity between two users according to their ratings to co-rated items. In many cases, two users rate only few common items, such that the similarity between them is inaccurate and it results in misleading recommendations. With the boost of social tagging systems, exploiting social tag information has been a popular way to improve recommender systems in recent years. In this paper, we propose a novel method to compute users' similarities using inferred tag ratings. A user's preference for a tag t can be inferred upon her ratings of items tagged with t . In a case that a user rates few such items, then the inferred rating to t may be inaccurate. Hence the relationships among tags are taken into consideration to compute her preference for t according to her all item ratings, such that the inferred preference of the user could be more accurate. Evaluations were done on the MovieLens data set. The results indicate that our method can outperform the traditional user-based collaborative filtering.

Research on Applying User-Based Collaborative Filtering Algorithms to Building Products Selection

Nowadays, there are lots of internet-based service platforms about construction products. The building products’ types are too many to select. This paper proposes an algorithm applying user-based collaborative filtering method to building products selection, which implements personalized recommendation with the help of group users’ experience. The algorithm provides decision support to relevant personnel and improves the efficiency and quality of building products selection.